diff --git a/.gitignore b/.gitignore
index b6e4761..798df2d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -127,3 +127,9 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+dataset/
+train/
+val/
+
+/backup.zip
\ No newline at end of file
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..ccdbe31
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,4 @@
+{
+    "markdown.preview.scrollPreviewWithEditor": true,
+    "markdown.preview.linkify": true
+}
\ No newline at end of file
diff --git a/ImageProcess.ipynb b/ImageProcess.ipynb
index 5e24c55..946b859 100644
--- a/ImageProcess.ipynb
+++ b/ImageProcess.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
     "    shutil.move(from_file_path, to_file_path)\n",
     "\n",
     "\n",
-    "def resize_image(img_path, IMAGE_WIDTH=1980, IMAGE_HEIGHT=1080):\n",
+    "def resize_image(img_path, IMAGE_WIDTH=1024, IMAGE_HEIGHT=1024):\n",
     "    for image in img_path:\n",
     "        img = Image.open(image)\n",
     "\n",
@@ -98,10 +98,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "path = './dataset/masks/'\n",
     "files = os.listdir(path)\n",
     "img_path = [path + file for file in files]\n",
     "\n",
@@ -109,6 +110,23 @@
     "resize_image(img_path)\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Increase Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for img in img_path:\n",
+    "    increase_image(img, degree=120, step=120)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -118,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,6 +194,11 @@
     "    shutil.move(os.path.join(mask_img_path, mask_imgs[i]), os.path.join(train_mask_path, mask_imgs[i]))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/README.md b/README.md
index 1ed2c9b..15df2b8 100644
--- a/README.md
+++ b/README.md
@@ -25,7 +25,7 @@ git clone https://github.com/TechC-SugarCane/SemanticSegmentation-demo
 `
 
 1. 以下のリンクをクリックしてデータセットをダウンロードします
-    - [dataset.zip 630MiB]("https://sugarcane.blob.core.windows.net/demo-dataset/dataset.zip")
+    - [dataset.zip 630MiB](https://sugarcane.blob.core.windows.net/demo-dataset/dataset.zip "dataset.zip 630MiB")
 
 1. ダウンロードしたデータセットを解凍してSemanticSegmentation-demoフォルダーの直下に保存します
 
@@ -33,11 +33,12 @@ git clone https://github.com/TechC-SugarCane/SemanticSegmentation-demo
 次のコマンドをコマンドを入力して必要なモジュールをすべてインストールします。
 
 `
-pip install -U requirements.txt
+pip install requirements.txt
 `
 
 インストールされているか確認する
-` module
+
+``` python
 import cv2
 import PIL
 import torch
@@ -55,7 +56,7 @@ print(smp.__version__)
 print(albumentations.__version__)
 print(glob.__version__)
 print(tqdm.__version__)
-`
+```
 
 ### 2 アノテーション
 
@@ -94,4 +95,4 @@ python json_to_img.py "jsonデータが保存されているパス" -o="保存
 
 ### 4, トレーニング
 
-train_model.ipynbにあるプログラムを上から実装してきます
\ No newline at end of file
+train_model.ipynbにあるプログラムを上から実装してきます
diff --git a/json_to_img.py b/json_to_img.py
index d497960..d601957 100644
--- a/json_to_img.py
+++ b/json_to_img.py
@@ -67,5 +67,5 @@
 print("Conversion is finished")
 
 """ run this command
-python json_to_img.py "./dataset/json/" -o="./dataset/masks/" -label_files="./dataset/label/label.txt"
+python json_to_img.py "./dataset/json/" -o="./dataset/masks/" -label_file="./dataset/label/label.txt"
 """
\ No newline at end of file
diff --git a/model/DeepLabV3_Plus_resnet34.pth b/model/DeepLabV3_Plus_resnet34.pth
new file mode 100644
index 0000000..83d048c
Binary files /dev/null and b/model/DeepLabV3_Plus_resnet34.pth differ
diff --git a/model/DeepLabV3_resnet34.pth b/model/DeepLabV3_resnet34.pth
new file mode 100644
index 0000000..db2d59f
Binary files /dev/null and b/model/DeepLabV3_resnet34.pth differ
diff --git a/model/PSP_net_resnet34.pth b/model/PSP_net_resnet34.pth
new file mode 100644
index 0000000..80a9fdf
Binary files /dev/null and b/model/PSP_net_resnet34.pth differ
diff --git a/model/unet_plus_plus_resnet34.pth b/model/unet_plus_plus_resnet34.pth
new file mode 100644
index 0000000..bfe5a11
Binary files /dev/null and b/model/unet_plus_plus_resnet34.pth differ
diff --git a/model/unet_resnet34.pth b/model/unet_resnet34.pth
new file mode 100644
index 0000000..e13a5c7
Binary files /dev/null and b/model/unet_resnet34.pth differ
diff --git a/plot_image/DeepLabV3+/example1.png b/plot_image/DeepLabV3+/example1.png
new file mode 100644
index 0000000..260b4b7
Binary files /dev/null and b/plot_image/DeepLabV3+/example1.png differ
diff --git a/plot_image/DeepLabV3+/example2.png b/plot_image/DeepLabV3+/example2.png
new file mode 100644
index 0000000..8c07a8b
Binary files /dev/null and b/plot_image/DeepLabV3+/example2.png differ
diff --git a/plot_image/DeepLabV3/example1.png b/plot_image/DeepLabV3/example1.png
new file mode 100644
index 0000000..898e583
Binary files /dev/null and b/plot_image/DeepLabV3/example1.png differ
diff --git a/plot_image/DeepLabV3/example2.png b/plot_image/DeepLabV3/example2.png
new file mode 100644
index 0000000..8c2702c
Binary files /dev/null and b/plot_image/DeepLabV3/example2.png differ
diff --git a/plot_image/U_Net++/example1.png b/plot_image/U_Net++/example1.png
new file mode 100644
index 0000000..b25e06b
Binary files /dev/null and b/plot_image/U_Net++/example1.png differ
diff --git a/plot_image/U_Net++/example2.png b/plot_image/U_Net++/example2.png
new file mode 100644
index 0000000..0b627c7
Binary files /dev/null and b/plot_image/U_Net++/example2.png differ
diff --git a/plot_image/U_Net/example1.png b/plot_image/U_Net/example1.png
new file mode 100644
index 0000000..6b693c8
Binary files /dev/null and b/plot_image/U_Net/example1.png differ
diff --git a/plot_image/U_Net/example2.png b/plot_image/U_Net/example2.png
new file mode 100644
index 0000000..6f0d5ca
Binary files /dev/null and b/plot_image/U_Net/example2.png differ
diff --git a/plot_image/psp_net/example1.png b/plot_image/psp_net/example1.png
new file mode 100644
index 0000000..cac26a0
Binary files /dev/null and b/plot_image/psp_net/example1.png differ
diff --git a/plot_image/psp_net/example2.png b/plot_image/psp_net/example2.png
new file mode 100644
index 0000000..23b61b3
Binary files /dev/null and b/plot_image/psp_net/example2.png differ
diff --git a/plot_image/readme.md b/plot_image/readme.md
new file mode 100644
index 0000000..96851fb
--- /dev/null
+++ b/plot_image/readme.md
@@ -0,0 +1,39 @@
+## モデルの性能の比較
+
+各モデルごとに作った推論モデルの比較。
+サトウキビが大きく映っている画像と、小さく映っている画像を推論モデルごとに保存しています。
+
+### 大きく映っている画像
+
+- Unet
+![Unet](./U_Net/example1.png)
+
+- Unet++
+![Unet++](./U_Net%2B%2B/example1.png)
+
+- PSPNet
+![PSPNet](./psp_net/example1.png)
+
+- DeepLabV3
+![DeepLabV3](./DeepLabV3/example1.png)
+
+- DeepLabV3+
+![DeepLabV3](./DeepLabV3%2B/example1.png)
+
+
+### 小さく映っている画像
+
+- Unet
+![Unet](./U_Net/example2.png)
+
+- Unet++
+![Unet++](./U_Net%2B%2B/example2.png)
+
+- PSPNet
+![PSPNet](./psp_net/example2.png)
+
+- DeepLabV3
+![DeepLabV3](./DeepLabV3/example2.png)
+
+- DeepLabV3+
+![DeepLabV3](./DeepLabV3%2B/example2.png)
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 95b0bed..29b9ca6 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,5 +6,4 @@ segmentation_models_pytorch
 albumentations
 labelme
 numpy
-glob
 tqdm
\ No newline at end of file
diff --git a/train_model.ipynb b/train_model.ipynb
index 4f1b0d1..d193595 100644
--- a/train_model.ipynb
+++ b/train_model.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,13 +13,17 @@
     "\n",
     "from torch.utils.data import DataLoader\n",
     "from torch.utils.data import Dataset as BaseDataset\n",
+    "from PIL import Image\n",
     "import torch\n",
     "import torch.nn as nn\n",
     "import torch.nn.functional as F\n",
+    "from torchvision import transforms as T\n",
+    "import torchvision\n",
     "import segmentation_models_pytorch as smp\n",
     "\n",
     "import albumentations as albu\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "from tqdm.notebook import tqdm"
    ]
   },
   {
@@ -31,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +50,7 @@
     "        plt.imshow(image)\n",
     "    plt.show()\n",
     "\n",
-    "# データの拡張用(画像データ足りなくなったときに使う)\n",
+    "# データの拡張用\n",
     "def get_training_augmentation():\n",
     "    IMAGE_SIZE = 256\n",
     "    train_transform = [\n",
@@ -87,7 +91,13 @@
     "        albu.Lambda(image=preprocessing_fn),\n",
     "        albu.Lambda(image=to_tensor, mask=to_tensor),\n",
     "    ]\n",
-    "    return albu.Compose(_transform)"
+    "    return albu.Compose(_transform)\n",
+    "\n",
+    "def crop_to_square(image):\n",
+    "    size = min(image.size)\n",
+    "    left, upper = (image.width - size) // 2, (image.height - size) // 2\n",
+    "    right, bottom = (image.width + size) // 2, (image.height + size) // 2\n",
+    "    return image.crop((left, upper, right, bottom))"
    ]
   },
   {
@@ -99,61 +109,184 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "class Dataset(BaseDataset):\n",
-    "    CLASSES=['background', 'weed']\n",
+    "# Old version\n",
+    "\n",
+    "# class Dataset(BaseDataset):\n",
+    "#     CLASSES=[ 'sugarcane', 'weed']\n",
     "    \n",
-    "    def __init__(self, images_dir, masks_dir, classes=None, augmentation=None, preprocessing=None):\n",
+    "#     def __init__(self, images_dir, masks_dir, classes, augmentation=None, preprocessing=None):\n",
+    "#         self.ids = os.listdir(images_dir)\n",
+    "#         self.images_fps = [os.path.join(images_dir, image_id) for image_id in self.ids]\n",
+    "#         self.masks_fps = [os.path.join(masks_dir, image_id) for image_id in self.ids]\n",
+    "#         # self.class_values =  [classes.index(cls.lower()) for cls in classes]\n",
+    "#         self.class_values =  classes\n",
+    "#         self.augmentation = augmentation\n",
+    "#         self.preprocessing = preprocessing\n",
+    "\n",
+    "#     def __getitem__(self, i):\n",
+    "\n",
+    "#         image = Image.open(self.images_fps[i])\n",
+    "#         image = crop_to_square(image)\n",
+    "#         image = image.resize((128, 128), Image.ANTIALIAS)\n",
+    "#         image = np.asarray(image)\n",
+    "\n",
+    "#         masks = Image.open(self.masks_fps[i])\n",
+    "#         masks = crop_to_square(masks)\n",
+    "#         masks = masks.resize((128, 128), Image.ANTIALIAS)\n",
+    "#         masks = np.asarray(masks)\n",
+    "\n",
+    "#         masks = np.where(masks == 255, 21, masks)\n",
+    "\n",
+    "#         cls_idx = [self.CLASSES.index(cls) for cls in self.class_values]\n",
+    "#         masks = [(masks == idx) for idx in cls_idx]\n",
+    "#         mask = np.stack(masks, axis=-1).astype(\"float32\")\n",
+    "\n",
+    "#         # # 画像データ\n",
+    "#         # image = cv2.imread(self.images_fps[i])\n",
+    "#         # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "\n",
+    "#         # # マスク画像データ\n",
+    "#         # mask = cv2.imread(self.masks_fps[i], 0)\n",
+    "#         # masks = [(mask == v) for v in self.class_values]\n",
+    "#         # mask = np.stack(masks, axis=-1).astype('float32')\n",
+    "\n",
+    "#         # データの拡張\n",
+    "#         if self.augmentation:\n",
+    "#             sample = self.augmentation(image=image, mask=mask)\n",
+    "#             image, mask = sample['image'], sample['mask']\n",
+    "            \n",
+    "#         if self.preprocessing:\n",
+    "#             sample = self.preprocessing(image=image, mask=mask)\n",
+    "#             image, mask = sample['image'], sample['mask']\n",
+    "\n",
+    "#         return image, mask\n",
+    "\n",
+    "#     def __len__(self):\n",
+    "#         return len(self.ids)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Dataset(BaseDataset):\n",
+    "    \"\"\"CamVid Dataset. Read images, apply augmentation and preprocessing transformations.\n",
+    "\n",
+    "    Args:\n",
+    "        images_dir (str): path to images folder\n",
+    "        masks_dir (str): path to segmentation masks folder\n",
+    "        class_values (list): values of classes to extract from segmentation mask\n",
+    "        augmentation (albumentations.Compose): data transfromation pipeline \n",
+    "            (e.g. flip, scale, etc.)\n",
+    "        preprocessing (albumentations.Compose): data preprocessing \n",
+    "            (e.g. noralization, shape manipulation, etc.)\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    mean=[0.485, 0.456, 0.406]\n",
+    "    std=[0.229, 0.224, 0.225]\n",
+    "\n",
+    "    CLASSES=[ 'sugarcane', 'weed']\n",
+    "\n",
+    "    def __init__(\n",
+    "            self, \n",
+    "            images_dir, \n",
+    "            masks_dir, \n",
+    "            classes=None, \n",
+    "            augmentation=None, \n",
+    "            preprocessing=None,\n",
+    "    ):\n",
     "        self.ids = os.listdir(images_dir)\n",
     "        self.images_fps = [os.path.join(images_dir, image_id) for image_id in self.ids]\n",
     "        self.masks_fps = [os.path.join(masks_dir, image_id) for image_id in self.ids]\n",
-    "        self.class_values =  [classes.index(cls.lower()) for cls in CLASSES]\n",
+    "\n",
+    "        # convert str names to class values on masks\n",
+    "        self.class_values = [self.CLASSES.index(cls.lower()) for cls in classes]\n",
+    "\n",
     "        self.augmentation = augmentation\n",
     "        self.preprocessing = preprocessing\n",
     "\n",
     "    def __getitem__(self, i):\n",
-    "        # 画像データ\n",
+    "\n",
+    "        # read data\n",
     "        image = cv2.imread(self.images_fps[i])\n",
     "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
-    "\n",
-    "        # マスク画像データ\n",
     "        mask = cv2.imread(self.masks_fps[i], 0)\n",
-    "        masks = [(mask == v) for v in self.class_values]\n",
-    "        mask = np.stack(masks, axis=-1).astype('float32')\n",
     "\n",
-    "        # データの拡張\n",
+    "\n",
+    "        # apply augmentations\n",
     "        if self.augmentation:\n",
     "            sample = self.augmentation(image=image, mask=mask)\n",
     "            image, mask = sample['image'], sample['mask']\n",
-    "            \n",
+    "\n",
+    "        # apply preprocessing\n",
     "        if self.preprocessing:\n",
     "            sample = self.preprocessing(image=image, mask=mask)\n",
     "            image, mask = sample['image'], sample['mask']\n",
     "\n",
+    "        t = T.Compose([T.ToTensor(), T.Normalize(self.mean, self.std)])\n",
+    "        image = t(image)\n",
+    "        mask = torch.from_numpy(mask).long()\n",
+    "\n",
     "        return image, mask\n",
     "\n",
     "    def __len__(self):\n",
-    "        return len(self.ids)\n"
+    "        return len(self.ids)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "ENCODER = 'resnet34'\n",
     "ENCODER_WEIGHTS = 'imagenet'\n",
-    "CLASSES = ['weed']\n",
+    "CLASSES = [ 'sugarcane', 'weed']\n",
     "ACTIVATION = 'sigmoid'\n",
     "\n",
     "device = 'cuda'\n",
-    "decoder = 'unet'\n",
-    "\n",
-    "model = smp.Unet(\n",
+    "decoder = 'DeepLabV3'\n",
+    "\n",
+    "# model = smp.Unet(\n",
+    "#     encoder_name=ENCODER,\n",
+    "#     encoder_weights=ENCODER_WEIGHTS,\n",
+    "#     encoder_depth=4,\n",
+    "#     decoder_channels=(128, 64, 32, 16),\n",
+    "#     classes=len(CLASSES),\n",
+    "#     activation=ACTIVATION\n",
+    "# )\n",
+    "\n",
+    "# model = smp.UnetPlusPlus(\n",
+    "#     encoder_name=ENCODER,\n",
+    "#     encoder_weights=ENCODER_WEIGHTS,\n",
+    "#     encoder_depth=4,\n",
+    "#     decoder_channels=(128, 64, 32, 16),\n",
+    "#     classes=len(CLASSES),\n",
+    "#     activation=ACTIVATION\n",
+    "# )\n",
+    "\n",
+    "# model = smp.PSPNet(\n",
+    "#     encoder_name=ENCODER,\n",
+    "#     encoder_weights=ENCODER_WEIGHTS,\n",
+    "#     classes=len(CLASSES),\n",
+    "#     activation=ACTIVATION\n",
+    "# )\n",
+    "\n",
+    "# model = smp.DeepLabV3Plus(\n",
+    "#     encoder_name=ENCODER,\n",
+    "#     encoder_weights=ENCODER_WEIGHTS,\n",
+    "#     classes=len(CLASSES),\n",
+    "#     activation=ACTIVATION\n",
+    "# )\n",
+    "\n",
+    "model = smp.DeepLabV3(\n",
     "    encoder_name=ENCODER,\n",
     "    encoder_weights=ENCODER_WEIGHTS,\n",
     "    classes=len(CLASSES),\n",
@@ -165,7 +298,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.parameters()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -183,90 +325,52 @@
     "    os.mkdir(val_dir + '/masks')"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\hokut\\AppData\\Roaming\\Python\\Python39\\site-packages\\albumentations\\imgaug\\transforms.py:262: FutureWarning: IAAAdditiveGaussianNoise is deprecated. Please use GaussNoise instead\n",
-      "  warnings.warn(\"IAAAdditiveGaussianNoise is deprecated. Please use GaussNoise instead\", FutureWarning)\n",
-      "C:\\Users\\hokut\\AppData\\Roaming\\Python\\Python39\\site-packages\\albumentations\\imgaug\\transforms.py:385: FutureWarning: This IAAPerspective is deprecated. Please use Perspective instead\n",
-      "  warnings.warn(\"This IAAPerspective is deprecated. Please use Perspective instead\", FutureWarning)\n",
-      "C:\\Users\\hokut\\AppData\\Roaming\\Python\\Python39\\site-packages\\albumentations\\augmentations\\transforms.py:1613: FutureWarning: This class has been deprecated. Please use RandomBrightnessContrast\n",
-      "  warnings.warn(\n",
-      "C:\\Users\\hokut\\AppData\\Roaming\\Python\\Python39\\site-packages\\albumentations\\imgaug\\transforms.py:232: FutureWarning: IAASharpen is deprecated. Please use Sharpen instead\n",
-      "  warnings.warn(\"IAASharpen is deprecated. Please use Sharpen instead\", FutureWarning)\n",
-      "C:\\Users\\hokut\\AppData\\Roaming\\Python\\Python39\\site-packages\\albumentations\\augmentations\\transforms.py:1639: FutureWarning: RandomContrast has been deprecated. Please use RandomBrightnessContrast\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_dir = './train'\n",
     "val_dir = './val'\n",
     "\n",
     "preprocessing_fn = smp.encoders.get_preprocessing_fn(ENCODER, ENCODER_WEIGHTS)\n",
     "\n",
+    "\n",
     "train_dataset = Dataset(\n",
     "    os.path.join(train_dir, 'images'),\n",
     "    os.path.join(train_dir, 'masks'),\n",
     "    augmentation=get_training_augmentation(),\n",
-    "    preprocessing=get_preproessing(preprocessing_fn),\n",
-    "    classes=['weed']\n",
+    "    # preprocessing=get_preproessing(preprocessing_fn),\n",
+    "    classes=CLASSES\n",
     ")\n",
     "\n",
     "valid_dataset = Dataset(\n",
     "    os.path.join(val_dir, 'images'),\n",
     "    os.path.join(val_dir, 'masks'),\n",
     "    augmentation=get_training_augmentation(),\n",
-    "    preprocessing=get_preproessing(preprocessing_fn),\n",
-    "    classes=['weed']\n",
+    "    # preprocessing=get_preproessing(preprocessing_fn),\n",
+    "    classes=CLASSES\n",
     ")\n",
     "\n",
-    "train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True, num_workers=0)\n",
-    "valid_loader = DataLoader(valid_dataset, batch_size=1, shuffle=False, num_workers=0)"
+    "train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True, num_workers=0, drop_last=True)\n",
+    "valid_loader = DataLoader(valid_dataset, batch_size=2, shuffle=False, num_workers=0, drop_last=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1024, 1024, 3)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1152x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "dataset = Dataset(os.path.join(train_dir, 'images'), os.path.join(train_dir, 'masks'), classes=['weed'])\n",
+    "dataset = Dataset(os.path.join(train_dir, 'images'), os.path.join(train_dir, 'masks'), classes=[ 'sugarcane', 'weed'])\n",
     "\n",
-    "image, mask = dataset[0]\n",
+    "image, mask = dataset[5]\n",
     "\n",
     "print(image.shape)\n",
+    "print(mask.shape)\n",
     "\n",
-    "visualzie(image=image, mask=mask.squeeze())"
+    "visualzie(image=image.permute(1, 2, 0), mask=mask)"
    ]
   },
   {
@@ -278,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,200 +421,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Epoch: 0\n",
-      "train: 100%|██████████| 11/11 [00:07<00:00,  1.39it/s, dice_loss - 0.3281, iou_score - 0.6939]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 20.72it/s, dice_loss - 0.8662, iou_score - 0.08518]\n",
-      "Model saved!\n",
-      "\n",
-      "Epoch: 1\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.23it/s, dice_loss - 0.214, iou_score - 0.9133] \n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 20.53it/s, dice_loss - 0.6567, iou_score - 0.2517]\n",
-      "Model saved!\n",
-      "\n",
-      "Epoch: 2\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.23it/s, dice_loss - 0.1617, iou_score - 0.9124]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.14it/s, dice_loss - 0.3439, iou_score - 0.6452]\n",
-      "Model saved!\n",
-      "\n",
-      "Epoch: 3\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.25it/s, dice_loss - 0.1084, iou_score - 0.9402]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 20.65it/s, dice_loss - 0.1212, iou_score - 0.8923] \n",
-      "Model saved!\n",
-      "\n",
-      "Epoch: 4\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.25it/s, dice_loss - 0.08274, iou_score - 0.9275]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 20.53it/s, dice_loss - 0.1193, iou_score - 0.8734]\n",
-      "\n",
-      "Epoch: 5\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.25it/s, dice_loss - 0.08373, iou_score - 0.9014]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 20.73it/s, dice_loss - 0.1139, iou_score - 0.8584]\n",
-      "\n",
-      "Epoch: 6\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.29it/s, dice_loss - 0.04842, iou_score - 0.9503]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.27it/s, dice_loss - 0.0552, iou_score - 0.9423] \n",
-      "Model saved!\n",
-      "\n",
-      "Epoch: 7\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.25it/s, dice_loss - 0.0555, iou_score - 0.9281]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.25it/s, dice_loss - 0.06757, iou_score - 0.9272]\n",
-      "\n",
-      "Epoch: 8\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.24it/s, dice_loss - 0.06168, iou_score - 0.9086]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.47it/s, dice_loss - 0.1072, iou_score - 0.8838] \n",
-      "\n",
-      "Epoch: 9\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.25it/s, dice_loss - 0.05597, iou_score - 0.9194]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.04it/s, dice_loss - 0.1143, iou_score - 0.8498] \n",
-      "\n",
-      "Epoch: 10\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.31it/s, dice_loss - 0.07203, iou_score - 0.8844]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 20.81it/s, dice_loss - 0.08396, iou_score - 0.8865]\n",
-      "\n",
-      "Epoch: 11\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.28it/s, dice_loss - 0.04003, iou_score - 0.9412]\n",
-      "valid: 100%|██████████| 43/43 [00:01<00:00, 21.68it/s, dice_loss - 0.05206, iou_score - 0.9331]\n",
-      "\n",
-      "Epoch: 12\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.30it/s, dice_loss - 0.05096, iou_score - 0.9137]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.29it/s, dice_loss - 0.06747, iou_score - 0.9222]\n",
-      "\n",
-      "Epoch: 13\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.26it/s, dice_loss - 0.03437, iou_score - 0.9481]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 20.77it/s, dice_loss - 0.06092, iou_score - 0.9151]\n",
-      "\n",
-      "Epoch: 14\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.28it/s, dice_loss - 0.04285, iou_score - 0.9281]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.11it/s, dice_loss - 0.03206, iou_score - 0.9492]\n",
-      "Model saved!\n",
-      "\n",
-      "Epoch: 15\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.26it/s, dice_loss - 0.03943, iou_score - 0.9346]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 20.77it/s, dice_loss - 0.05868, iou_score - 0.917] \n",
-      "\n",
-      "Epoch: 16\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.25it/s, dice_loss - 0.05459, iou_score - 0.9052]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.06it/s, dice_loss - 0.101, iou_score - 0.8562] \n",
-      "\n",
-      "Epoch: 17\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.30it/s, dice_loss - 0.04513, iou_score - 0.9241]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.21it/s, dice_loss - 0.1279, iou_score - 0.849] \n",
-      "\n",
-      "Epoch: 18\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.28it/s, dice_loss - 0.05126, iou_score - 0.9173]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.48it/s, dice_loss - 0.05647, iou_score - 0.9152]\n",
-      "\n",
-      "Epoch: 19\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.26it/s, dice_loss - 0.04215, iou_score - 0.9308]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.39it/s, dice_loss - 0.08856, iou_score - 0.8779]\n",
-      "\n",
-      "Epoch: 20\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.27it/s, dice_loss - 0.03029, iou_score - 0.9492]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.14it/s, dice_loss - 0.1198, iou_score - 0.8344]\n",
-      "\n",
-      "Epoch: 21\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.25it/s, dice_loss - 0.03825, iou_score - 0.9328]\n",
-      "valid: 100%|██████████| 43/43 [00:01<00:00, 21.58it/s, dice_loss - 0.2018, iou_score - 0.7416]\n",
-      "\n",
-      "Epoch: 22\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.23it/s, dice_loss - 0.05083, iou_score - 0.9084]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.48it/s, dice_loss - 0.04057, iou_score - 0.9371]\n",
-      "\n",
-      "Epoch: 23\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.24it/s, dice_loss - 0.06363, iou_score - 0.8982]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.41it/s, dice_loss - 0.02974, iou_score - 0.9521]\n",
-      "Model saved!\n",
-      "\n",
-      "Epoch: 24\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.25it/s, dice_loss - 0.06628, iou_score - 0.8903]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.26it/s, dice_loss - 0.04487, iou_score - 0.9329]\n",
-      "\n",
-      "Epoch: 25\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.26it/s, dice_loss - 0.03126, iou_score - 0.946]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.09it/s, dice_loss - 0.03757, iou_score - 0.9513]\n",
-      "\n",
-      "Epoch: 26\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.24it/s, dice_loss - 0.04422, iou_score - 0.9222]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.19it/s, dice_loss - 0.08672, iou_score - 0.8878]\n",
-      "\n",
-      "Epoch: 27\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.25it/s, dice_loss - 0.03198, iou_score - 0.9422]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.14it/s, dice_loss - 0.093, iou_score - 0.8824]  \n",
-      "\n",
-      "Epoch: 28\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.29it/s, dice_loss - 0.02911, iou_score - 0.9477]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 20.74it/s, dice_loss - 0.05417, iou_score - 0.922] \n",
-      "\n",
-      "Epoch: 29\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.24it/s, dice_loss - 0.04688, iou_score - 0.9148]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.01it/s, dice_loss - 0.07842, iou_score - 0.893] \n",
-      "\n",
-      "Epoch: 30\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.23it/s, dice_loss - 0.1096, iou_score - 0.8595] \n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 20.84it/s, dice_loss - 0.06996, iou_score - 0.9061]\n",
-      "\n",
-      "Epoch: 31\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.23it/s, dice_loss - 0.06559, iou_score - 0.8893]\n",
-      "valid: 100%|██████████| 43/43 [00:01<00:00, 21.51it/s, dice_loss - 0.03681, iou_score - 0.9392]\n",
-      "\n",
-      "Epoch: 32\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.26it/s, dice_loss - 0.0334, iou_score - 0.9386]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.36it/s, dice_loss - 0.05728, iou_score - 0.9229]\n",
-      "\n",
-      "Epoch: 33\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.23it/s, dice_loss - 0.07737, iou_score - 0.8647]\n",
-      "valid: 100%|██████████| 43/43 [00:01<00:00, 21.52it/s, dice_loss - 0.07728, iou_score - 0.9]   \n",
-      "\n",
-      "Epoch: 34\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.24it/s, dice_loss - 0.08442, iou_score - 0.8709]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 20.83it/s, dice_loss - 0.04157, iou_score - 0.938] \n",
-      "\n",
-      "Epoch: 35\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.25it/s, dice_loss - 0.03511, iou_score - 0.9423]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.44it/s, dice_loss - 0.05449, iou_score - 0.9172]\n",
-      "\n",
-      "Epoch: 36\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.25it/s, dice_loss - 0.03931, iou_score - 0.9303]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 18.95it/s, dice_loss - 0.0484, iou_score - 0.9187] \n",
-      "\n",
-      "Epoch: 37\n",
-      "train: 100%|██████████| 11/11 [00:05<00:00,  2.16it/s, dice_loss - 0.03798, iou_score - 0.9302]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 20.50it/s, dice_loss - 0.05224, iou_score - 0.9188]\n",
-      "\n",
-      "Epoch: 38\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.24it/s, dice_loss - 0.02737, iou_score - 0.9495]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.29it/s, dice_loss - 0.02541, iou_score - 0.957] \n",
-      "Model saved!\n",
-      "\n",
-      "Epoch: 39\n",
-      "train: 100%|██████████| 11/11 [00:04<00:00,  2.25it/s, dice_loss - 0.02543, iou_score - 0.9532]\n",
-      "valid: 100%|██████████| 43/43 [00:02<00:00, 21.03it/s, dice_loss - 0.0333, iou_score - 0.9455] \n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "max_score = 0\n",
-    "epoch = 40\n",
+    "\n",
+    "patience = 5\n",
+    "early_stop_counter = 0\n",
+    "\n",
+    "epoch = 50\n",
     "for i in range(epoch):\n",
     "    print(f'\\nEpoch: {i}')\n",
-    "    try:\n",
-    "        train_logs = train_epoch.run(train_loader)\n",
-    "        val_logs = valid_epoch.run(valid_loader)\n",
-    "    except Exception as e:\n",
-    "        print(e)\n",
-    "        break\n",
+    "    # try:\n",
+    "    #     train_logs = train_epoch.run(train_loader)\n",
+    "    #     val_logs = valid_epoch.run(valid_loader)\n",
+    "    # except Exception as e:\n",
+    "    #     print(e)\n",
+    "    #     break\n",
+    "    train_logs = train_epoch.run(train_loader)\n",
+    "    val_logs = valid_epoch.run(valid_loader)\n",
     "    \n",
     "    if max_score < val_logs['iou_score']:\n",
     "        max_score = val_logs['iou_score']\n",
-    "        torch.save(model, f'{decoder}_{ENCODER}.pth')\n",
-    "        print('Model saved!')"
+    "        torch.save(model, f'./model/{decoder}_{ENCODER}.pth')\n",
+    "    #     print('Model saved!')\n",
+    "    #     early_stop_counter = 0\n",
+    "    # else:\n",
+    "    #     early_stop_counter += 1\n",
+    "    #     print(f\"not improve for {early_stop_counter} Epoch\")\n",
+    "    #     if early_stop_counter==patience:\n",
+    "    #         print(f\"early stop. Max Score {max_score}\")\n",
+    "    #         break\n",
+    "\n",
+    "    if i == 30:\n",
+    "        optimizer.param_groups[0]['lr'] = 1e-4\n",
+    "        print('Decrease decoder learning rate to 1e-4!')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"max_score: {max_score:.2f}\")"
    ]
   },
   {
@@ -522,10207 +477,131 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "val_files = glob.glob('val/images/*')\n",
-    "f = val_files[5]\n",
-    "image_src = cv2.imread(f)\n",
-    "image_src = cv2.cvtColor(image_src, cv2.COLOR_BGR2RGB)\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import cv2\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from torch.utils.data import Dataset as BaseDataset\n",
+    "from torchvision import transforms as T\n",
+    "import torchvision\n",
+    "import torch.nn.functional as F\n",
+    "from torch.autograd import Variable\n",
     "\n",
-    "image = preprocessing_fn(image_src)\n",
-    "image = image.transpose(2, 0, 1).astype('float32')\n",
+    "from PIL import Image\n",
+    "import cv2\n",
+    "import albumentations as albu\n",
     "\n",
-    "image = torch.from_numpy(image).to(device).unsqueeze(0)\n",
-    "predict = model(image)\n",
-    "predict = predict.detach().cpu().numpy()[0].reshape((1024, 1024))\n",
+    "import time\n",
+    "import os\n",
+    "from tqdm.notebook import tqdm\n",
     "\n",
-    "predict_img = np.zeros([1024, 1024]).astype(np.int8)\n",
-    "predict_img = np.where(predict> 0.5, 1, predict_img)"
+    "from torch.utils.data import Dataset as BaseDataset\n",
+    "import segmentation_models_pytorch as smp\n",
+    "import glob"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x1df8ce1cd00>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "fig = plt.figure(figsize=(12, 6))\n",
-    "ax1 = fig.add_subplot(1, 2, 1)\n",
-    "ax2 = fig.add_subplot(1, 2, 2)\n",
-    "ax1.imshow(image_src)\n",
-    "ax2.imshow(predict_img)"
+    "CLASSES=[ 'sugarcane', 'weed']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Parameter containing:\n",
-       " tensor([[[[ 6.6996e-03, -1.4011e-02,  4.2771e-04,  ...,  4.2391e-02,\n",
-       "             2.6735e-02,  2.6081e-02],\n",
-       "           [ 4.6284e-02,  2.5740e-02,  2.4774e-02,  ...,  2.7344e-02,\n",
-       "             2.5501e-02,  4.3924e-02],\n",
-       "           [ 9.3874e-03, -3.9363e-02, -6.6128e-02,  ..., -1.0259e-01,\n",
-       "            -1.1872e-01, -1.1704e-01],\n",
-       "           ...,\n",
-       "           [-1.2763e-02, -3.3707e-02, -1.3609e-02,  ...,  1.3299e-02,\n",
-       "             2.5101e-03,  1.8618e-02],\n",
-       "           [ 3.8530e-03, -2.9744e-03,  3.2956e-02,  ...,  1.0123e-01,\n",
-       "             7.4383e-02,  6.2001e-02],\n",
-       "           [ 1.8038e-02,  6.0355e-03,  3.1257e-02,  ...,  9.7282e-02,\n",
-       "             8.4329e-02,  1.0258e-01]],\n",
-       " \n",
-       "          [[-1.0880e-02, -1.9504e-02,  2.8822e-03,  ...,  2.3561e-02,\n",
-       "             1.7849e-02,  2.1396e-02],\n",
-       "           [ 5.3421e-02,  3.4463e-02,  2.1974e-02,  ...,  2.6495e-03,\n",
-       "             9.3507e-03,  3.4892e-02],\n",
-       "           [-5.5614e-04, -6.7607e-02, -1.1450e-01,  ..., -2.0345e-01,\n",
-       "            -2.0937e-01, -1.8283e-01],\n",
-       "           ...,\n",
-       "           [-2.3503e-02, -4.2566e-02, -1.3764e-02,  ..., -1.5629e-02,\n",
-       "            -4.1184e-02, -1.5004e-02],\n",
-       "           [ 1.3967e-02,  1.7237e-02,  7.2200e-02,  ...,  1.5523e-01,\n",
-       "             1.1332e-01,  8.1958e-02],\n",
-       "           [ 3.7302e-02,  3.5963e-02,  7.5628e-02,  ...,  1.7155e-01,\n",
-       "             1.3934e-01,  1.3560e-01]],\n",
-       " \n",
-       "          [[-9.0156e-03, -2.5145e-02, -2.7685e-02,  ..., -1.9993e-02,\n",
-       "            -5.8015e-03,  8.0854e-03],\n",
-       "           [ 6.2010e-02,  3.3650e-02,  2.4489e-02,  ...,  1.8620e-02,\n",
-       "             3.2148e-02,  5.9206e-02],\n",
-       "           [ 2.8074e-02, -1.9672e-02, -3.6825e-02,  ..., -8.6379e-02,\n",
-       "            -8.8050e-02, -8.2848e-02],\n",
-       "           ...,\n",
-       "           [ 7.2378e-03, -8.2060e-03,  1.1968e-02,  ...,  8.7714e-03,\n",
-       "            -8.6537e-03,  1.5535e-02],\n",
-       "           [ 1.2764e-02,  1.0065e-02,  4.7099e-02,  ...,  9.7174e-02,\n",
-       "             6.4829e-02,  3.8956e-02],\n",
-       "           [ 2.1493e-02,  8.5996e-03,  3.8630e-02,  ...,  1.0653e-01,\n",
-       "             8.2349e-02,  7.0904e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 4.7890e-03, -2.8388e-02,  3.7498e-02,  ..., -1.3745e-01,\n",
-       "            -2.5821e-02,  2.1659e-02],\n",
-       "           [ 6.8074e-03, -8.6494e-02,  7.6209e-02,  ..., -2.7187e-01,\n",
-       "             1.3210e-02,  5.7907e-02],\n",
-       "           [ 8.8997e-03, -1.1360e-01,  8.9903e-02,  ..., -4.8102e-01,\n",
-       "             8.4617e-02,  6.6670e-02],\n",
-       "           ...,\n",
-       "           [-1.8953e-02, -5.8466e-02,  3.7620e-02,  ..., -5.1664e-01,\n",
-       "             1.8154e-01, -1.4599e-02],\n",
-       "           [-2.1865e-02, -4.8229e-02, -8.4719e-04,  ..., -2.8989e-01,\n",
-       "             8.6639e-02, -1.8873e-02],\n",
-       "           [ 2.4221e-02, -3.6017e-02,  1.4749e-02,  ..., -1.5931e-01,\n",
-       "            -5.6740e-03, -9.4441e-03]],\n",
-       " \n",
-       "          [[ 2.7547e-02, -1.9709e-02,  6.6717e-02,  ..., -1.3231e-01,\n",
-       "            -6.8961e-02,  2.3673e-02],\n",
-       "           [ 7.5420e-03, -8.7544e-02,  1.4435e-01,  ..., -3.3894e-01,\n",
-       "            -4.5672e-02,  1.1412e-01],\n",
-       "           [ 1.1445e-02, -1.1204e-01,  1.9879e-01,  ..., -6.5721e-01,\n",
-       "             3.4969e-02,  1.6704e-01],\n",
-       "           ...,\n",
-       "           [-6.9286e-03, -5.0921e-02,  1.0151e-01,  ..., -6.6764e-01,\n",
-       "             1.8621e-01,  8.1499e-02],\n",
-       "           [ 1.5163e-02, -2.2701e-02,  3.3633e-02,  ..., -3.4964e-01,\n",
-       "             1.1291e-01,  4.2038e-02],\n",
-       "           [ 2.0045e-02, -3.2234e-02,  1.1982e-02,  ..., -1.5035e-01,\n",
-       "            -1.0259e-03,  2.0057e-02]],\n",
-       " \n",
-       "          [[-1.4728e-02, -4.2176e-02,  2.5837e-04,  ..., -7.4524e-02,\n",
-       "            -3.1704e-02, -3.8424e-02],\n",
-       "           [-2.1229e-02, -7.7357e-02,  8.2108e-03,  ..., -1.5835e-01,\n",
-       "             9.3507e-03, -3.4730e-02],\n",
-       "           [ 5.2907e-05, -7.5099e-02, -5.3921e-03,  ..., -3.1252e-01,\n",
-       "             8.5056e-02, -3.7132e-02],\n",
-       "           ...,\n",
-       "           [-1.6673e-02, -3.4732e-02, -6.3134e-02,  ..., -3.6905e-01,\n",
-       "             1.7657e-01, -7.5622e-02],\n",
-       "           [-8.9438e-03, -2.6721e-02, -6.5094e-02,  ..., -2.2014e-01,\n",
-       "             8.7683e-02, -5.6703e-02],\n",
-       "           [-5.7280e-03, -3.1506e-02, -3.0466e-02,  ..., -1.1354e-01,\n",
-       "             7.9779e-03, -2.6953e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-7.2829e-03, -2.0215e-02, -4.7618e-02,  ...,  1.7606e-02,\n",
-       "             1.3283e-01, -6.1405e-02],\n",
-       "           [-2.3417e-04,  3.2228e-02,  9.5598e-02,  ..., -3.2527e-01,\n",
-       "            -1.4899e-01,  1.8768e-01],\n",
-       "           [-9.1057e-03, -3.5234e-02, -1.6076e-01,  ...,  4.7765e-01,\n",
-       "            -2.8337e-01, -1.9152e-01],\n",
-       "           ...,\n",
-       "           [-2.0197e-03, -6.7772e-02,  1.7421e-01,  ..., -5.3751e-01,\n",
-       "            -1.8566e-01,  1.5327e-01],\n",
-       "           [-1.1185e-02,  1.5694e-03, -1.5129e-01,  ...,  2.2531e-01,\n",
-       "            -1.4262e-01, -1.0728e-01],\n",
-       "           [-2.1716e-02, -1.5800e-02,  1.2409e-02,  ..., -9.9736e-03,\n",
-       "             6.8204e-02, -7.6498e-03]],\n",
-       " \n",
-       "          [[ 2.7027e-02,  1.8225e-02, -1.6613e-02,  ..., -1.8274e-02,\n",
-       "             1.1586e-01, -5.8432e-02],\n",
-       "           [-1.3362e-02,  3.4062e-02,  1.4502e-01,  ..., -3.4894e-01,\n",
-       "            -2.3869e-01,  1.3118e-01],\n",
-       "           [-5.6949e-02, -7.9817e-02, -1.9948e-01,  ...,  7.1567e-01,\n",
-       "            -2.3450e-01, -2.6390e-01],\n",
-       "           ...,\n",
-       "           [ 3.1754e-02, -5.3733e-02,  1.8171e-01,  ..., -6.9869e-01,\n",
-       "            -2.1790e-01,  2.1966e-01],\n",
-       "           [ 9.0790e-03,  2.6685e-02, -1.3556e-01,  ...,  2.0182e-01,\n",
-       "            -1.9473e-01, -1.0250e-01],\n",
-       "           [-1.7552e-02,  1.5468e-02,  4.3658e-02,  ..., -1.4198e-02,\n",
-       "             5.3564e-02, -2.0502e-02]],\n",
-       " \n",
-       "          [[ 2.1183e-02, -6.6722e-03, -3.9252e-02,  ...,  3.6857e-03,\n",
-       "             9.7626e-02, -7.1229e-02],\n",
-       "           [ 1.6725e-02,  3.5584e-02,  9.7142e-02,  ..., -3.0783e-01,\n",
-       "            -1.1479e-01,  1.8152e-01],\n",
-       "           [-1.6509e-02, -1.8409e-02, -1.2819e-01,  ...,  3.8938e-01,\n",
-       "            -3.0263e-01, -1.7542e-01],\n",
-       "           ...,\n",
-       "           [-3.3948e-04, -9.3456e-02,  1.1626e-01,  ..., -3.8400e-01,\n",
-       "            -1.2190e-01,  1.5338e-01],\n",
-       "           [ 5.5365e-04,  5.2749e-03, -1.7200e-01,  ...,  2.0567e-01,\n",
-       "            -9.5505e-02, -1.4492e-02],\n",
-       "           [-1.4371e-02, -4.5831e-05,  2.7104e-02,  ..., -6.8828e-02,\n",
-       "             2.6870e-02, -6.9613e-03]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-1.0210e-02,  5.8983e-03, -2.2266e-02,  ...,  5.1711e-03,\n",
-       "            -2.7308e-02, -2.8699e-02],\n",
-       "           [-2.0504e-02,  1.9104e-02, -6.6556e-02,  ...,  1.4906e-01,\n",
-       "            -2.7147e-02, -3.5762e-02],\n",
-       "           [-3.6344e-02,  9.6662e-02,  1.1912e-01,  ..., -7.1519e-02,\n",
-       "             2.0486e-01, -4.3450e-02],\n",
-       "           ...,\n",
-       "           [ 6.4087e-02, -2.5068e-01, -3.2820e-02,  ..., -2.0450e-01,\n",
-       "            -2.8730e-01,  6.5760e-02],\n",
-       "           [ 1.2698e-02,  2.5163e-02, -2.7787e-01,  ...,  2.1173e-01,\n",
-       "            -1.3893e-01, -7.8569e-02],\n",
-       "           [-3.6284e-02,  3.6197e-02, -1.4446e-02,  ...,  4.7418e-02,\n",
-       "             1.6086e-02, -4.6952e-02]],\n",
-       " \n",
-       "          [[ 4.2378e-03, -3.4394e-03, -3.3856e-02,  ..., -1.7002e-02,\n",
-       "             1.3230e-03, -1.0665e-02],\n",
-       "           [-2.5117e-02,  2.0226e-02, -8.6194e-02,  ...,  1.0998e-01,\n",
-       "             1.3080e-02,  5.5836e-03],\n",
-       "           [-3.1873e-02,  1.3332e-01,  1.7256e-01,  ..., -2.1757e-01,\n",
-       "             2.1231e-01, -2.6155e-02],\n",
-       "           ...,\n",
-       "           [ 2.1638e-02, -3.5603e-01,  5.3829e-02,  ..., -2.1831e-01,\n",
-       "            -3.9742e-01,  2.6187e-02],\n",
-       "           [ 7.2995e-03, -5.9601e-02, -3.5824e-01,  ...,  2.9135e-01,\n",
-       "            -1.5307e-01, -1.0477e-01],\n",
-       "           [-1.6686e-02, -2.6406e-03, -1.1004e-01,  ...,  7.0303e-02,\n",
-       "             4.1687e-02, -2.9199e-02]],\n",
-       " \n",
-       "          [[ 1.2710e-02,  8.8846e-03,  1.9441e-03,  ..., -4.9908e-02,\n",
-       "            -3.0530e-02, -3.2646e-03],\n",
-       "           [-6.9711e-03,  5.5996e-03, -3.0848e-02,  ...,  1.0141e-01,\n",
-       "            -4.9382e-02, -1.5477e-02],\n",
-       "           [-4.7604e-02,  5.2655e-02,  9.5318e-02,  ..., -2.9462e-02,\n",
-       "             1.2539e-01, -9.8903e-02],\n",
-       "           ...,\n",
-       "           [ 5.5148e-02, -2.3502e-01, -9.8733e-02,  ..., -1.1863e-01,\n",
-       "            -1.9346e-01,  4.2669e-02],\n",
-       "           [ 5.8481e-03,  3.9714e-02, -2.7337e-01,  ...,  1.7889e-01,\n",
-       "            -8.3091e-02, -6.2829e-02],\n",
-       "           [-1.4924e-02,  8.4183e-03, -5.8742e-02,  ...,  1.4943e-02,\n",
-       "             4.4138e-02, -4.4756e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-7.3239e-02, -7.7487e-02, -9.1820e-02,  ..., -6.8881e-02,\n",
-       "            -7.2716e-02, -4.3166e-02],\n",
-       "           [-8.7057e-02, -3.2487e-02, -3.5824e-02,  ..., -1.1144e-02,\n",
-       "            -2.8644e-02, -2.1995e-02],\n",
-       "           [-2.3305e-02,  5.3965e-02,  5.3666e-02,  ...,  3.8805e-02,\n",
-       "             3.1409e-02,  3.0636e-02],\n",
-       "           ...,\n",
-       "           [ 7.0435e-02,  1.2673e-01,  5.4121e-02,  ...,  2.3132e-02,\n",
-       "             1.6505e-02,  8.1556e-02],\n",
-       "           [ 1.9516e-02,  2.2960e-02, -4.0903e-02,  ..., -6.3538e-02,\n",
-       "            -4.4740e-02,  1.6537e-02],\n",
-       "           [ 3.4781e-03, -7.8155e-03, -5.7250e-02,  ..., -6.2357e-02,\n",
-       "            -3.1854e-02,  1.9467e-02]],\n",
-       " \n",
-       "          [[-9.0609e-02, -9.2646e-02, -9.9914e-02,  ..., -6.8758e-02,\n",
-       "            -6.8585e-02, -5.3524e-02],\n",
-       "           [-8.9005e-02, -5.9434e-02, -4.0734e-02,  ...,  1.4471e-02,\n",
-       "            -1.7702e-02, -2.7717e-02],\n",
-       "           [-1.7154e-02,  3.2795e-02,  5.1675e-02,  ...,  6.0854e-02,\n",
-       "             3.8678e-02,  2.6248e-02],\n",
-       "           ...,\n",
-       "           [ 5.0718e-02,  9.9118e-02,  4.8686e-02,  ...,  3.0892e-02,\n",
-       "             1.1542e-02,  6.7751e-02],\n",
-       "           [ 1.0711e-02,  1.6079e-02, -3.3141e-02,  ..., -5.7649e-02,\n",
-       "            -5.8815e-02,  3.7184e-03],\n",
-       "           [-2.7721e-02, -3.5313e-02, -7.5914e-02,  ..., -7.1654e-02,\n",
-       "            -5.1363e-02, -8.5653e-04]],\n",
-       " \n",
-       "          [[-2.3913e-02, -2.7197e-02, -4.9358e-02,  ..., -1.7350e-02,\n",
-       "            -1.6866e-02,  1.7948e-02],\n",
-       "           [-4.8892e-02, -1.4708e-02, -3.7189e-02,  ...,  4.6269e-04,\n",
-       "            -2.0571e-02, -1.3408e-02],\n",
-       "           [-2.9612e-03,  3.9487e-02,  2.2279e-02,  ...,  2.3836e-02,\n",
-       "             6.2828e-03,  5.7580e-03],\n",
-       "           ...,\n",
-       "           [ 4.6539e-02,  9.0763e-02,  3.0198e-02,  ...,  3.1909e-02,\n",
-       "             1.1912e-02,  7.6458e-02],\n",
-       "           [ 3.2755e-02,  5.2910e-02, -1.4558e-03,  ..., -1.4075e-02,\n",
-       "            -1.1123e-02,  4.6261e-02],\n",
-       "           [ 1.5911e-02,  1.1131e-02, -1.0904e-02,  ...,  2.6907e-03,\n",
-       "             1.3204e-02,  6.1133e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 3.8198e-09,  5.2821e-09,  8.4708e-09,  ...,  9.3896e-09,\n",
-       "             1.0989e-08,  8.6680e-09],\n",
-       "           [ 1.0561e-08,  1.0032e-08,  1.2596e-08,  ...,  9.4018e-09,\n",
-       "             1.0029e-08,  1.3073e-08],\n",
-       "           [ 1.0009e-08,  6.9546e-09,  7.0832e-09,  ...,  1.1455e-08,\n",
-       "             8.3313e-09,  1.4117e-08],\n",
-       "           ...,\n",
-       "           [ 8.9857e-09,  6.0239e-09,  6.9334e-09,  ...,  9.9647e-09,\n",
-       "             8.1298e-09,  1.3150e-08],\n",
-       "           [ 9.3205e-09,  8.4864e-09,  1.0279e-08,  ...,  1.0536e-08,\n",
-       "             1.3432e-08,  1.2343e-08],\n",
-       "           [ 1.2704e-08,  1.2244e-08,  1.0365e-08,  ...,  1.3496e-08,\n",
-       "             1.1680e-08,  1.4884e-08]],\n",
-       " \n",
-       "          [[-1.4684e-09, -6.0630e-10,  2.0655e-09,  ...,  3.2167e-09,\n",
-       "             5.7001e-09,  4.5557e-09],\n",
-       "           [ 5.5310e-09,  5.3149e-09,  7.1034e-09,  ...,  3.6033e-09,\n",
-       "             7.9869e-09,  8.5333e-09],\n",
-       "           [ 6.1580e-09,  3.8952e-09,  1.4952e-09,  ...,  2.3825e-09,\n",
-       "             5.0903e-09,  6.6121e-09],\n",
-       "           ...,\n",
-       "           [ 4.4388e-09,  4.0306e-09,  4.0409e-09,  ...,  5.2286e-09,\n",
-       "             1.4921e-09,  2.7369e-09],\n",
-       "           [ 7.9369e-09,  4.0945e-09,  2.5078e-09,  ...,  6.5217e-09,\n",
-       "             4.4083e-09,  5.1524e-09],\n",
-       "           [ 7.3701e-09,  4.7664e-09,  4.5817e-09,  ...,  3.6753e-09,\n",
-       "             5.5694e-09,  5.8749e-09]],\n",
-       " \n",
-       "          [[-8.0702e-09, -7.5842e-09, -5.0519e-09,  ..., -7.1475e-09,\n",
-       "            -6.9767e-09, -2.3015e-09],\n",
-       "           [ 8.2218e-10,  8.6029e-10, -7.5594e-10,  ..., -2.4140e-09,\n",
-       "             2.3130e-09,  4.2510e-09],\n",
-       "           [ 2.6394e-10, -1.6171e-09, -1.8819e-10,  ..., -1.5929e-09,\n",
-       "             2.5107e-10,  1.5924e-09],\n",
-       "           ...,\n",
-       "           [ 6.8491e-09, -1.8575e-09, -1.7964e-09,  ..., -5.0751e-10,\n",
-       "             9.6112e-10,  3.7407e-09],\n",
-       "           [ 8.7351e-10,  2.8883e-10, -7.9156e-10,  ..., -3.7688e-10,\n",
-       "            -2.9927e-09, -5.4954e-10],\n",
-       "           [ 4.8446e-09,  1.9827e-09,  2.2090e-09,  ...,  2.5378e-09,\n",
-       "             2.0013e-09,  1.1245e-09]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([3.2685e-01, 2.8667e-01, 2.3371e-01, 3.1731e-01, 2.7186e-01, 3.4989e-01,\n",
-       "         2.9793e-01, 3.9947e-01, 4.1767e-01, 4.0228e-01, 2.6326e-01, 3.2097e-01,\n",
-       "         4.3105e-01, 2.6974e-01, 2.1761e-01, 1.1412e-06, 1.5042e-06, 2.8900e-01,\n",
-       "         3.0109e-01, 4.3385e-01, 2.9204e-01, 2.6524e-01, 2.6558e-01, 5.9221e-01,\n",
-       "         3.0994e-01, 1.3868e-01, 2.5284e-01, 3.3935e-01, 3.3513e-01, 2.1696e-01,\n",
-       "         3.3076e-01, 1.0375e-03, 2.5186e-01, 3.4974e-01, 7.3461e-02, 3.4426e-06,\n",
-       "         3.2518e-01, 2.9482e-01, 3.8956e-01, 2.6498e-01, 2.6736e-01, 6.6502e-08,\n",
-       "         2.7692e-01, 2.0791e-01, 3.2502e-01, 4.0320e-01, 1.3414e-06, 2.8622e-05,\n",
-       "         5.6135e-01, 2.9142e-06, 6.3189e-01, 2.1011e-01, 2.7700e-01, 3.1175e-01,\n",
-       "         2.3022e-01, 3.9340e-01, 3.3102e-01, 8.0925e-05, 3.2416e-01, 1.8137e-01,\n",
-       "         1.4748e-01, 2.6307e-01, 3.3967e-01, 3.9389e-08], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 5.0218e-01,  2.1745e-01,  3.2895e-01,  4.1274e-01,  7.9543e-02,\n",
-       "          2.4928e-01,  1.5520e-01,  8.2681e-01,  7.3904e-01, -1.7278e-01,\n",
-       "          2.7767e-01,  3.1879e-01, -1.2807e-01,  1.8739e-01,  3.2619e-01,\n",
-       "         -4.9471e-06, -4.8600e-06,  2.2548e-01,  1.7762e-01, -3.5450e-01,\n",
-       "          3.6530e-01,  2.6743e-01,  1.8337e-01, -4.3454e-01,  2.8027e-01,\n",
-       "          1.9164e-01,  5.4344e-01,  6.2496e-01,  4.4297e-01,  2.6381e-01,\n",
-       "          5.6850e-01, -1.4239e-02,  1.5995e-01,  4.0029e-01,  3.3576e-02,\n",
-       "         -1.1473e-05,  5.7278e-01,  3.2559e-01,  4.7319e-01,  2.0558e-01,\n",
-       "          2.5340e-01, -2.1946e-07,  2.6645e-01,  5.3151e-02,  1.8883e-01,\n",
-       "         -4.7663e-01, -4.6348e-06, -8.8477e-05, -6.0141e-01, -9.9434e-06,\n",
-       "         -3.2666e-01,  2.6145e-01,  3.8265e-01,  4.9126e-01,  2.9431e-01,\n",
-       "          4.9392e-01,  2.5733e-01, -2.3575e-04,  4.9102e-01,  2.6514e-01,\n",
-       "         -1.5235e-01,  2.1219e-01,  7.3827e-01, -1.3984e-07], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 3.0404e-03,  2.8961e-02,  1.0089e-02],\n",
-       "           [-5.5941e-02, -1.2772e-02, -2.9263e-03],\n",
-       "           [ 1.8693e-02,  1.4111e-03,  4.6901e-02]],\n",
-       " \n",
-       "          [[-1.2312e-02,  2.1483e-03, -1.2262e-02],\n",
-       "           [ 3.2242e-03,  2.9792e-02,  1.1054e-02],\n",
-       "           [ 2.0937e-02,  2.9107e-03, -5.1352e-03]],\n",
-       " \n",
-       "          [[-5.7246e-02, -3.2152e-02, -5.3742e-02],\n",
-       "           [ 1.1581e-03, -2.1098e-02, -2.7776e-02],\n",
-       "           [-7.9100e-03,  2.4259e-03, -6.6552e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 3.8974e-03,  6.6739e-03,  1.6605e-02],\n",
-       "           [-1.8926e-02,  6.4837e-02, -4.4839e-03],\n",
-       "           [ 4.4590e-02, -1.0623e-02, -1.1662e-02]],\n",
-       " \n",
-       "          [[ 1.1257e-02,  1.0955e-02, -1.9418e-02],\n",
-       "           [ 3.2323e-02,  2.8595e-02,  4.2108e-02],\n",
-       "           [-4.3333e-02, -3.6232e-02, -1.3558e-02]],\n",
-       " \n",
-       "          [[ 7.3316e-10,  2.8611e-09,  7.1522e-09],\n",
-       "           [ 5.0183e-09,  1.0337e-08,  1.1351e-08],\n",
-       "           [ 7.8760e-09,  1.4317e-09, -2.0827e-09]]],\n",
-       " \n",
-       " \n",
-       "         [[[-4.4886e-02, -3.1469e-02, -3.0328e-02],\n",
-       "           [-8.1259e-02, -1.2389e-01, -9.0184e-02],\n",
-       "           [ 2.2201e-02,  9.3696e-02,  4.5326e-02]],\n",
-       " \n",
-       "          [[-1.6369e-02, -2.5046e-02,  1.5271e-02],\n",
-       "           [-6.4105e-03, -3.3834e-02,  2.2772e-03],\n",
-       "           [-1.0605e-02,  4.2695e-02,  2.7781e-02]],\n",
-       " \n",
-       "          [[ 7.7641e-03,  3.3222e-03,  2.3349e-02],\n",
-       "           [ 2.1662e-02, -1.2182e-02,  3.9778e-02],\n",
-       "           [ 3.0975e-02,  8.0951e-04, -1.0910e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.1519e-02,  3.6991e-02, -5.4862e-03],\n",
-       "           [ 2.1715e-02, -6.2260e-02,  6.6873e-02],\n",
-       "           [-3.8812e-03,  6.8909e-03, -2.0120e-02]],\n",
-       " \n",
-       "          [[ 6.1272e-02,  9.2151e-02,  8.7262e-02],\n",
-       "           [-1.4363e-02,  3.0339e-02,  5.2954e-04],\n",
-       "           [-2.6222e-02, -7.8831e-02, -7.4605e-02]],\n",
-       " \n",
-       "          [[ 1.9127e-09, -5.1764e-09, -5.7548e-09],\n",
-       "           [-2.7478e-09, -4.4095e-09, -1.3663e-08],\n",
-       "           [-1.0954e-08, -1.5885e-08, -1.1000e-08]]],\n",
-       " \n",
-       " \n",
-       "         [[[-3.5114e-02, -3.1882e-02, -2.8431e-02],\n",
-       "           [ 4.9076e-02,  3.6606e-02,  4.3464e-02],\n",
-       "           [-1.6911e-02, -1.5870e-02, -3.1931e-02]],\n",
-       " \n",
-       "          [[ 2.3553e-02,  2.5797e-02, -7.5228e-03],\n",
-       "           [ 1.6019e-02, -2.2278e-01,  1.6194e-02],\n",
-       "           [-4.0767e-03, -2.1171e-02, -1.5392e-02]],\n",
-       " \n",
-       "          [[ 4.2847e-02,  3.9411e-02, -1.6492e-02],\n",
-       "           [-3.5742e-02, -2.2060e-02,  1.8494e-02],\n",
-       "           [-1.3978e-02,  6.9765e-03, -1.8816e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.1831e-02,  9.0792e-02,  1.0534e-02],\n",
-       "           [-2.4855e-02, -1.5388e-01,  2.3179e-03],\n",
-       "           [-4.4599e-02,  5.0910e-02, -2.0229e-02]],\n",
-       " \n",
-       "          [[-1.8367e-02, -5.4206e-02, -1.5481e-02],\n",
-       "           [ 1.2370e-02, -7.5116e-02,  1.7210e-02],\n",
-       "           [-1.1735e-02, -5.6507e-02, -2.3779e-03]],\n",
-       " \n",
-       "          [[-2.2675e-09, -1.1453e-08, -4.4594e-09],\n",
-       "           [-4.7696e-09, -2.5766e-09,  2.1014e-09],\n",
-       "           [ 7.1414e-09, -4.0140e-09, -3.1576e-10]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 1.7520e-02,  6.3231e-02,  2.3479e-02],\n",
-       "           [ 5.7879e-02,  5.9678e-02,  6.5504e-02],\n",
-       "           [ 1.0491e-02, -8.9304e-04, -9.5297e-03]],\n",
-       " \n",
-       "          [[-4.8273e-02, -9.9922e-02,  4.7681e-03],\n",
-       "           [-1.3847e-01, -3.2390e-01, -3.7773e-02],\n",
-       "           [-7.2272e-02, -1.2057e-01, -2.8717e-03]],\n",
-       " \n",
-       "          [[ 1.9293e-02,  1.2155e-03,  3.2745e-02],\n",
-       "           [-1.2933e-02, -2.7800e-02,  1.7138e-03],\n",
-       "           [-7.1386e-03,  3.5160e-02,  4.3885e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.8506e-02, -9.8996e-03,  8.9332e-03],\n",
-       "           [-6.3354e-02, -1.7973e-01, -5.0261e-02],\n",
-       "           [ 2.5759e-02, -1.3869e-02,  4.6198e-02]],\n",
-       " \n",
-       "          [[ 2.7168e-02,  4.8937e-02, -7.3683e-03],\n",
-       "           [ 1.4632e-02,  2.9844e-02, -1.2092e-02],\n",
-       "           [ 8.1063e-03,  1.5261e-02, -3.3713e-02]],\n",
-       " \n",
-       "          [[-2.9362e-09,  3.9263e-09, -7.3836e-09],\n",
-       "           [-1.8761e-09, -4.7639e-09, -5.3289e-09],\n",
-       "           [-2.7958e-09, -6.4469e-09, -5.8980e-09]]],\n",
-       " \n",
-       " \n",
-       "         [[[-6.7794e-03,  3.0128e-03,  7.4988e-02],\n",
-       "           [-8.4434e-02, -9.6098e-03,  1.7273e-01],\n",
-       "           [-9.3643e-02,  4.3359e-03,  8.7674e-02]],\n",
-       " \n",
-       "          [[-1.0402e-03, -3.8911e-02,  3.6021e-02],\n",
-       "           [ 6.4963e-03, -1.2731e-01,  4.7285e-02],\n",
-       "           [ 1.4291e-02, -5.6880e-02,  6.5911e-02]],\n",
-       " \n",
-       "          [[ 5.5770e-03, -1.8358e-02, -7.0461e-03],\n",
-       "           [ 8.1202e-03, -4.2413e-02, -3.0787e-04],\n",
-       "           [ 1.4971e-02,  1.1265e-02, -6.1522e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 7.7207e-03,  2.5889e-02,  8.3421e-03],\n",
-       "           [-6.2971e-04, -6.5605e-02, -2.3781e-02],\n",
-       "           [-6.0233e-03,  2.7156e-02,  1.9538e-02]],\n",
-       " \n",
-       "          [[ 1.3623e-01, -2.0431e-02, -8.4205e-02],\n",
-       "           [ 1.9789e-01, -4.4506e-03, -1.1579e-01],\n",
-       "           [ 1.5053e-01, -1.5531e-02, -8.5692e-02]],\n",
-       " \n",
-       "          [[ 9.3762e-09,  1.4521e-09, -8.9437e-09],\n",
-       "           [ 1.2228e-08,  1.1994e-09, -1.1223e-08],\n",
-       "           [ 4.3606e-09, -6.2485e-09, -1.1025e-08]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.0219e-02,  7.4930e-03,  7.3734e-02],\n",
-       "           [ 2.6639e-02, -1.1340e-03, -1.9449e-02],\n",
-       "           [ 5.1168e-03,  6.7602e-04,  5.7630e-02]],\n",
-       " \n",
-       "          [[ 2.7104e-02, -4.1715e-02,  6.9082e-03],\n",
-       "           [-1.3957e-02,  9.4861e-02,  4.2640e-03],\n",
-       "           [ 5.6663e-03,  1.1884e-02,  2.8440e-04]],\n",
-       " \n",
-       "          [[-1.0363e-02, -2.5634e-02,  3.2806e-02],\n",
-       "           [ 4.5361e-02,  3.1169e-02, -2.3915e-03],\n",
-       "           [ 5.2757e-04,  4.4359e-02,  1.2494e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.6768e-02, -6.3536e-02,  3.5010e-02],\n",
-       "           [-6.8554e-03,  5.7780e-02, -6.3206e-03],\n",
-       "           [ 4.9634e-03, -2.9593e-02,  2.7668e-03]],\n",
-       " \n",
-       "          [[ 2.6844e-02, -1.6916e-02,  3.8600e-02],\n",
-       "           [-7.7175e-02, -8.0898e-02,  2.0218e-02],\n",
-       "           [-9.2656e-03, -1.0505e-02,  5.7832e-02]],\n",
-       " \n",
-       "          [[-4.0727e-10, -3.4332e-09, -1.5047e-09],\n",
-       "           [-2.3499e-09, -3.6365e-09,  1.2890e-09],\n",
-       "           [ 2.1744e-09,  2.3404e-09,  6.7831e-09]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2584, 0.2002, 0.2082, 0.1628, 0.2145, 0.3122, 0.2464, 0.3378, 0.2335,\n",
-       "         0.1822, 0.3140, 0.3906, 0.2671, 0.2372, 0.3170, 0.3135, 0.3055, 0.4572,\n",
-       "         0.3733, 0.3119, 0.2188, 0.2798, 0.3771, 0.4153, 0.2113, 0.2108, 0.4014,\n",
-       "         0.3691, 0.3186, 0.1458, 0.2415, 0.1719, 0.2245, 0.2466, 0.1457, 0.3162,\n",
-       "         0.2572, 0.2277, 0.2073, 0.2173, 0.4301, 0.3786, 0.2684, 0.2802, 0.2229,\n",
-       "         0.4689, 0.4167, 0.3427, 0.3081, 0.2231, 0.2070, 0.2927, 0.1754, 0.2610,\n",
-       "         0.4591, 0.1945, 0.2652, 0.2714, 0.2162, 0.3996, 0.1450, 0.4231, 0.3921,\n",
-       "         0.1915], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.0094,  0.0760,  0.1003,  0.1209,  0.0700,  0.1391, -0.2462, -0.1301,\n",
-       "         -0.1375,  0.1830,  0.0416, -0.3076, -0.1522,  0.0948,  0.0294, -0.2074,\n",
-       "         -0.4911, -0.2550, -0.1387,  0.0010, -0.0058, -0.1495, -0.1548, -0.3693,\n",
-       "          0.0784,  0.0049, -0.3883, -0.1066, -0.0909,  0.2377, -0.3015,  0.1438,\n",
-       "          0.0787,  0.0708,  0.1170,  0.0074, -0.2506,  0.1092,  0.0611,  0.1164,\n",
-       "         -0.1538, -0.1101,  0.0244,  0.0417,  0.1337, -0.1577, -0.1819,  0.0605,\n",
-       "          0.0440,  0.0388,  0.1156, -0.4368,  0.1920,  0.0425, -0.1458,  0.2295,\n",
-       "         -0.0673, -0.0264,  0.0959, -0.1523,  0.3110, -0.2049,  0.0060,  0.1146],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 3.1713e-02, -4.2527e-02,  1.0950e-02],\n",
-       "           [ 2.8816e-03, -8.6527e-02, -1.4514e-02],\n",
-       "           [ 3.6625e-03,  3.8611e-02,  4.3144e-02]],\n",
-       " \n",
-       "          [[ 1.9025e-02,  9.0442e-02,  1.1130e-03],\n",
-       "           [ 1.7662e-02, -4.4366e-02,  1.6778e-02],\n",
-       "           [ 5.4128e-02, -8.5306e-02,  6.0612e-02]],\n",
-       " \n",
-       "          [[-2.7734e-02, -4.8653e-02, -6.0903e-02],\n",
-       "           [-2.5284e-02,  1.2665e-02, -1.0917e-02],\n",
-       "           [ 5.7376e-03,  1.9060e-02, -3.5746e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.1279e-02, -6.6794e-02, -4.6400e-02],\n",
-       "           [-1.0035e-02, -3.4157e-02,  6.4172e-03],\n",
-       "           [-5.3866e-03, -8.9918e-03,  2.0187e-02]],\n",
-       " \n",
-       "          [[ 1.3406e-03, -4.5134e-03, -2.0510e-02],\n",
-       "           [-9.6353e-03, -4.7769e-02, -3.4957e-02],\n",
-       "           [ 1.2997e-02, -2.2593e-02, -2.5357e-02]],\n",
-       " \n",
-       "          [[ 2.2537e-02,  1.3281e-02,  1.3531e-02],\n",
-       "           [-1.0531e-02,  3.8664e-02,  4.2691e-02],\n",
-       "           [ 5.7781e-02,  4.7162e-02,  4.3189e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 3.9694e-04,  4.8790e-03, -3.6740e-02],\n",
-       "           [ 7.0587e-03,  2.7979e-02, -2.9261e-02],\n",
-       "           [-1.4645e-02, -3.1802e-03, -2.8985e-02]],\n",
-       " \n",
-       "          [[ 1.2865e-02,  1.2900e-02,  9.2546e-03],\n",
-       "           [ 2.1136e-02,  7.9144e-03,  2.1151e-02],\n",
-       "           [-4.3435e-03, -7.9396e-03,  3.8106e-03]],\n",
-       " \n",
-       "          [[-4.0291e-04,  4.4154e-03, -1.4720e-02],\n",
-       "           [-1.5020e-02, -7.7897e-02, -3.5780e-02],\n",
-       "           [ 9.3166e-03,  1.2988e-02,  1.1619e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.8330e-02, -4.6217e-02, -2.2137e-02],\n",
-       "           [-5.5508e-02, -1.1484e-01, -7.0280e-02],\n",
-       "           [-1.9953e-02, -5.2364e-02, -3.6412e-02]],\n",
-       " \n",
-       "          [[ 5.6088e-02,  6.0347e-03,  2.1565e-02],\n",
-       "           [ 5.9799e-02, -5.2567e-03,  1.8215e-03],\n",
-       "           [ 8.3334e-02,  4.0307e-02,  3.4718e-02]],\n",
-       " \n",
-       "          [[-5.5478e-02, -2.1288e-02, -2.5027e-02],\n",
-       "           [-3.4142e-02,  1.6121e-02, -5.5836e-03],\n",
-       "           [-9.7049e-03,  4.7186e-04, -5.9601e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-4.3643e-02, -1.3735e-02, -2.2972e-04],\n",
-       "           [-4.1365e-03,  1.3001e-02,  1.2939e-02],\n",
-       "           [-4.1682e-02, -2.6409e-02, -9.7939e-03]],\n",
-       " \n",
-       "          [[ 7.7328e-03, -3.9169e-02,  1.1300e-02],\n",
-       "           [ 2.4450e-02, -2.2373e-02, -2.6715e-02],\n",
-       "           [ 1.7919e-02,  3.3115e-02,  2.4947e-02]],\n",
-       " \n",
-       "          [[ 7.1244e-02,  3.3412e-02,  1.4577e-02],\n",
-       "           [ 5.2036e-02,  8.9860e-03,  1.6402e-02],\n",
-       "           [ 2.9927e-02,  7.6721e-02,  1.5658e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 4.3446e-02,  5.0397e-02,  2.2800e-02],\n",
-       "           [ 2.2278e-03, -1.8645e-02,  9.4855e-03],\n",
-       "           [ 4.1675e-02, -2.3581e-02,  1.0209e-02]],\n",
-       " \n",
-       "          [[-1.5580e-02,  1.5954e-02,  6.9774e-03],\n",
-       "           [-3.4189e-02, -1.1044e-03,  2.5900e-02],\n",
-       "           [-3.7766e-03,  2.6402e-02,  1.3236e-03]],\n",
-       " \n",
-       "          [[-4.6196e-02, -4.4195e-02, -5.2907e-02],\n",
-       "           [-4.4022e-02, -1.0009e-02, -4.5059e-02],\n",
-       "           [ 2.1807e-02, -3.1130e-02, -3.8769e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-2.9735e-02, -4.5421e-02, -5.0345e-02],\n",
-       "           [-1.4899e-02, -3.8532e-03, -2.6369e-02],\n",
-       "           [-3.9484e-02, -4.1490e-02, -4.0125e-02]],\n",
-       " \n",
-       "          [[-6.6274e-03,  3.6025e-03,  1.5716e-02],\n",
-       "           [ 1.4230e-02, -6.4794e-03,  1.6823e-02],\n",
-       "           [ 1.2605e-02, -6.7022e-03,  6.9228e-03]],\n",
-       " \n",
-       "          [[-8.8220e-03, -3.5840e-02, -1.7571e-02],\n",
-       "           [-3.6750e-03, -4.0075e-02, -1.2038e-02],\n",
-       "           [ 1.1517e-02,  1.2115e-02, -8.1061e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 9.2253e-03,  3.9267e-03,  1.4014e-02],\n",
-       "           [-1.5688e-02, -3.3832e-02, -1.3084e-02],\n",
-       "           [-1.2127e-03, -1.0316e-02,  1.2282e-03]],\n",
-       " \n",
-       "          [[ 6.8622e-03, -1.4266e-02, -9.9491e-03],\n",
-       "           [-1.4931e-02, -2.9814e-02, -2.7533e-02],\n",
-       "           [ 3.5313e-03, -4.0808e-03, -1.1189e-02]],\n",
-       " \n",
-       "          [[-1.4289e-02, -1.5844e-03, -7.7856e-04],\n",
-       "           [-7.5912e-03,  1.2548e-02,  7.0364e-03],\n",
-       "           [-1.2500e-02, -3.1641e-03,  4.5789e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-5.4118e-02,  7.5637e-03, -3.9194e-02],\n",
-       "           [-5.3186e-02,  3.5219e-02, -9.9990e-03],\n",
-       "           [-3.9848e-02, -8.8993e-02, -9.2717e-02]],\n",
-       " \n",
-       "          [[-4.0218e-02, -7.6212e-02,  4.1485e-03],\n",
-       "           [ 3.3935e-02,  5.2481e-02, -1.3759e-02],\n",
-       "           [-5.3019e-02, -4.9256e-02, -4.6332e-02]],\n",
-       " \n",
-       "          [[-3.9520e-02, -3.7837e-02,  7.9310e-03],\n",
-       "           [-8.5454e-02,  1.8824e-01, -1.2517e-01],\n",
-       "           [-5.3880e-02, -5.4227e-02, -8.4089e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-8.5841e-02, -4.4831e-02, -1.4245e-02],\n",
-       "           [-3.9687e-02,  9.3847e-02, -4.7516e-02],\n",
-       "           [-4.3753e-02,  5.9561e-03, -5.2142e-02]],\n",
-       " \n",
-       "          [[ 5.4710e-03,  3.7755e-02,  2.5035e-03],\n",
-       "           [ 6.9944e-03,  8.9700e-02, -3.6022e-02],\n",
-       "           [-2.8385e-02, -3.0538e-02, -1.1642e-02]],\n",
-       " \n",
-       "          [[-5.2820e-02,  1.0429e-01, -1.2723e-02],\n",
-       "           [ 4.2622e-02, -1.6761e-01,  1.8805e-02],\n",
-       "           [-9.5043e-02,  8.7986e-02, -4.7111e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 6.5872e-02,  7.2022e-02,  6.5054e-02],\n",
-       "           [ 7.8448e-02,  7.5742e-02,  5.2660e-02],\n",
-       "           [ 5.3488e-02,  4.7873e-02,  1.6445e-02]],\n",
-       " \n",
-       "          [[-8.1402e-03, -3.7541e-02, -2.4095e-02],\n",
-       "           [ 6.4908e-03,  2.1937e-03, -1.0320e-02],\n",
-       "           [-2.3397e-02, -3.3519e-02, -2.5636e-02]],\n",
-       " \n",
-       "          [[ 1.6469e-02,  1.7174e-04, -1.2454e-02],\n",
-       "           [-1.6185e-03, -5.1977e-03, -3.9549e-02],\n",
-       "           [ 5.3422e-03, -1.1758e-02, -2.5240e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 3.2802e-03,  1.4041e-04, -2.4491e-02],\n",
-       "           [ 8.5997e-03,  2.4887e-02, -1.0479e-02],\n",
-       "           [ 1.2690e-03,  1.6036e-02, -3.3249e-04]],\n",
-       " \n",
-       "          [[ 1.0512e-03,  1.3435e-02,  1.8528e-02],\n",
-       "           [ 1.9218e-02,  4.6955e-02,  2.8731e-02],\n",
-       "           [ 2.0444e-04,  2.8144e-02,  2.6929e-02]],\n",
-       " \n",
-       "          [[-6.2911e-03, -2.4749e-02, -3.2738e-02],\n",
-       "           [ 1.2578e-02, -1.1418e-02, -2.7169e-02],\n",
-       "           [-2.7249e-02, -3.2566e-02, -1.5722e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.3460, 0.1891, 0.2684, 0.3158, 0.2648, 0.1377, 0.3776, 0.0315, 0.1013,\n",
-       "         0.1604, 0.0529, 0.3516, 0.1481, 0.1843, 0.1881, 0.2695, 0.2973, 0.1679,\n",
-       "         0.3358, 0.2145, 0.3261, 0.2988, 0.2110, 0.1711, 0.0682, 0.3988, 0.4713,\n",
-       "         0.4023, 0.3351, 0.2240, 0.2349, 0.2614, 0.1212, 0.2827, 0.3501, 0.3870,\n",
-       "         0.1252, 0.3777, 0.2259, 0.1390, 0.2357, 0.4286, 0.1513, 0.2690, 0.3035,\n",
-       "         0.2124, 0.3331, 0.3156, 0.2605, 0.2257, 0.1883, 0.1734, 0.4234, 0.3388,\n",
-       "         0.1046, 0.1051, 0.2364, 0.3263, 0.3228, 0.1996, 0.2603, 0.1083, 0.4603,\n",
-       "         0.2316], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.2297,  0.2100,  0.2135, -0.0945,  0.0912,  0.0273, -0.1009, -0.0278,\n",
-       "         -0.1134,  0.1658,  0.0126, -0.0341,  0.1250,  0.2837,  0.2724,  0.1418,\n",
-       "         -0.1280, -0.0119,  0.1872, -0.3154, -0.0373,  0.2180,  0.0230, -0.3608,\n",
-       "          0.0823, -0.2085,  0.1262, -0.0473,  0.0389,  0.1333, -0.2175,  0.3388,\n",
-       "         -0.1064, -0.2994,  0.0524, -0.0366,  0.0056,  0.0604, -0.0312, -0.0984,\n",
-       "          0.1749, -0.1250,  0.1406,  0.0056,  0.2803, -0.2271, -0.0126,  0.0045,\n",
-       "         -0.4639,  0.3035, -0.0552,  0.1414,  0.1773,  0.1318, -0.0125,  0.0462,\n",
-       "          0.2799, -0.0372,  0.0880,  0.1037,  0.2275, -0.0127,  0.4064,  0.3245],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-4.0252e-02, -6.6278e-02, -7.7261e-02],\n",
-       "           [-3.6032e-02, -3.8614e-03, -5.3956e-02],\n",
-       "           [-2.8423e-03, -4.7829e-02, -5.8916e-02]],\n",
-       " \n",
-       "          [[ 5.3405e-03,  3.7536e-02,  4.6888e-03],\n",
-       "           [ 2.5571e-02, -7.8745e-03, -3.1069e-02],\n",
-       "           [ 2.0656e-02, -6.1495e-03,  1.8702e-02]],\n",
-       " \n",
-       "          [[ 7.8289e-04,  2.3550e-02, -2.1510e-02],\n",
-       "           [-2.8568e-02, -2.1724e-02, -4.1782e-02],\n",
-       "           [ 2.2275e-02, -3.0769e-02,  3.3151e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.6810e-02,  3.2196e-02, -1.5168e-02],\n",
-       "           [-2.3605e-03, -3.8239e-02,  3.7509e-04],\n",
-       "           [ 4.2904e-02, -2.0539e-02,  8.1748e-02]],\n",
-       " \n",
-       "          [[ 1.6559e-03,  5.4295e-02, -2.6024e-02],\n",
-       "           [-5.6091e-02, -8.7135e-02, -2.2603e-02],\n",
-       "           [ 3.6913e-02,  8.3248e-02,  4.4190e-02]],\n",
-       " \n",
-       "          [[ 5.5624e-02,  4.7256e-02,  3.8674e-02],\n",
-       "           [ 3.7635e-02,  1.3019e-02,  2.5444e-02],\n",
-       "           [ 1.8385e-02, -1.1245e-03, -2.2992e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 2.9842e-02,  2.9056e-02,  5.2422e-02],\n",
-       "           [ 4.8222e-02,  7.3959e-02, -7.9388e-03],\n",
-       "           [-3.1105e-02, -7.6239e-04,  1.9714e-03]],\n",
-       " \n",
-       "          [[ 2.6637e-02, -5.4409e-02, -2.7359e-02],\n",
-       "           [ 1.5926e-02, -1.0443e-01, -5.7845e-02],\n",
-       "           [ 2.0670e-02, -3.1126e-02,  2.6851e-03]],\n",
-       " \n",
-       "          [[ 4.8072e-03, -4.0270e-03,  1.4987e-02],\n",
-       "           [-4.6679e-02, -5.4720e-02, -5.8179e-05],\n",
-       "           [ 9.3968e-03, -5.3985e-03, -1.8839e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-3.3054e-03, -7.1548e-02, -3.1921e-03],\n",
-       "           [ 6.3205e-03, -9.4349e-02, -6.3472e-02],\n",
-       "           [ 4.0764e-03,  1.3545e-02, -4.5372e-02]],\n",
-       " \n",
-       "          [[-9.2690e-02, -5.9038e-02,  2.1171e-02],\n",
-       "           [-7.8288e-02,  1.0050e-01,  4.8397e-02],\n",
-       "           [ 4.7430e-03, -5.2324e-02, -5.0593e-02]],\n",
-       " \n",
-       "          [[-1.8778e-02,  1.0986e-02,  3.8901e-02],\n",
-       "           [-1.9531e-02,  1.3896e-02,  3.4600e-02],\n",
-       "           [-2.5984e-02, -5.1878e-03,  1.4290e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 6.5922e-03, -2.7275e-02,  2.3773e-02],\n",
-       "           [ 5.6855e-02, -1.3933e-02,  3.9470e-02],\n",
-       "           [ 7.1494e-03, -1.0802e-02,  1.9974e-02]],\n",
-       " \n",
-       "          [[-1.7648e-02, -4.2666e-02,  3.1997e-02],\n",
-       "           [ 5.3918e-02, -1.1059e-01,  3.6135e-02],\n",
-       "           [ 4.5024e-02, -4.0471e-03,  8.3747e-02]],\n",
-       " \n",
-       "          [[-1.4340e-02,  7.6117e-02, -4.1706e-03],\n",
-       "           [ 8.6509e-02, -2.4711e-01, -6.2115e-02],\n",
-       "           [ 4.1911e-02, -6.5817e-02,  6.9328e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.0984e-03,  3.1730e-03, -1.0008e-02],\n",
-       "           [ 3.2058e-02, -2.2698e-01, -1.6328e-01],\n",
-       "           [-2.7328e-03, -1.0995e-01, -7.9517e-02]],\n",
-       " \n",
-       "          [[-2.7337e-02, -3.9362e-02,  4.3798e-02],\n",
-       "           [-9.6110e-02,  9.2198e-02,  3.9796e-02],\n",
-       "           [-4.1360e-02, -3.6791e-02, -1.2479e-01]],\n",
-       " \n",
-       "          [[-4.3415e-02, -1.0197e-02, -1.7479e-02],\n",
-       "           [-2.8970e-02,  3.7537e-02,  3.1179e-02],\n",
-       "           [-1.5824e-02,  3.3678e-02,  3.4697e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 3.2181e-02, -1.9452e-02,  2.7165e-02],\n",
-       "           [ 2.1039e-02, -4.0212e-02,  2.1281e-02],\n",
-       "           [ 5.3812e-02,  2.1766e-02, -3.0215e-03]],\n",
-       " \n",
-       "          [[ 3.0736e-02,  1.5589e-02,  5.0751e-02],\n",
-       "           [ 3.3352e-02,  1.0339e-01,  3.4577e-02],\n",
-       "           [ 6.2547e-02, -2.8467e-03,  9.5834e-03]],\n",
-       " \n",
-       "          [[ 1.3138e-02, -3.1043e-02, -1.4601e-02],\n",
-       "           [-3.6928e-03, -1.1270e-02, -2.5282e-02],\n",
-       "           [-2.3599e-02, -2.8485e-02, -4.0440e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 5.7893e-02,  1.5942e-02,  8.2863e-03],\n",
-       "           [-1.3608e-03,  4.8306e-02,  3.1540e-02],\n",
-       "           [ 4.0393e-02,  1.5481e-02,  4.4357e-02]],\n",
-       " \n",
-       "          [[ 4.8387e-02, -5.3907e-02,  3.6735e-02],\n",
-       "           [ 6.1420e-02, -8.9246e-02,  3.8112e-02],\n",
-       "           [ 6.5641e-03, -2.4964e-02,  3.7400e-02]],\n",
-       " \n",
-       "          [[ 3.2941e-02,  2.1910e-02,  4.6326e-02],\n",
-       "           [ 3.8133e-02,  2.3393e-02,  3.5501e-02],\n",
-       "           [ 3.3597e-03, -4.0873e-03,  7.5846e-04]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.0622e-01, -4.0577e-02, -2.1938e-02],\n",
-       "           [ 9.0934e-02, -1.3296e-02, -1.6429e-02],\n",
-       "           [ 3.5343e-02, -9.2688e-04,  6.7303e-02]],\n",
-       " \n",
-       "          [[ 3.2337e-02, -1.8163e-02, -2.1437e-05],\n",
-       "           [-3.1115e-02,  1.3910e-02,  3.8563e-02],\n",
-       "           [ 1.5074e-02, -7.3142e-03, -3.1061e-03]],\n",
-       " \n",
-       "          [[-8.6063e-02, -4.3937e-02,  9.4288e-02],\n",
-       "           [ 9.5403e-02, -4.2181e-02, -4.8737e-02],\n",
-       "           [-6.1658e-03, -4.0299e-02,  7.9142e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-8.1420e-03, -8.6068e-02, -3.6604e-03],\n",
-       "           [-3.9173e-02,  9.1051e-03, -7.4093e-02],\n",
-       "           [-4.4526e-02, -4.0623e-02, -2.7416e-03]],\n",
-       " \n",
-       "          [[ 1.9157e-03,  5.0853e-03, -1.8033e-02],\n",
-       "           [ 1.8788e-02, -1.2623e-01,  4.9320e-02],\n",
-       "           [ 2.0196e-02, -1.3216e-02, -8.6522e-02]],\n",
-       " \n",
-       "          [[-2.3108e-02,  2.8542e-02,  8.0821e-02],\n",
-       "           [-3.6800e-02,  2.0640e-02,  8.7030e-02],\n",
-       "           [ 1.2886e-02,  2.8084e-03,  2.9779e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-2.4322e-02, -6.3275e-02, -7.0609e-02],\n",
-       "           [-2.5567e-02,  5.2916e-02, -4.4582e-02],\n",
-       "           [-2.9323e-02,  1.7775e-02, -5.5420e-02]],\n",
-       " \n",
-       "          [[-4.4157e-02, -9.3146e-03, -3.4050e-02],\n",
-       "           [-5.1877e-02, -4.3570e-02, -7.6977e-03],\n",
-       "           [-2.1572e-02, -1.1708e-02,  2.1917e-02]],\n",
-       " \n",
-       "          [[ 1.6088e-03,  1.7092e-02,  1.5961e-02],\n",
-       "           [-8.3977e-03, -1.6304e-02, -6.6954e-02],\n",
-       "           [-1.2421e-02,  4.1291e-03, -4.1797e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-5.2692e-03, -1.1193e-02, -1.3699e-02],\n",
-       "           [ 9.3766e-03,  5.6020e-03, -1.9458e-02],\n",
-       "           [-2.6640e-02, -3.4273e-03, -3.7768e-02]],\n",
-       " \n",
-       "          [[-4.7465e-02,  2.2239e-03, -3.3710e-02],\n",
-       "           [-2.5448e-02, -5.4255e-02, -6.6829e-03],\n",
-       "           [ 5.1783e-03,  4.7583e-02,  4.0861e-04]],\n",
-       " \n",
-       "          [[-1.0512e-02,  6.5546e-04,  1.0719e-02],\n",
-       "           [ 2.1074e-02,  1.6410e-02,  3.8777e-02],\n",
-       "           [ 2.3539e-02,  1.2141e-02,  1.6743e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.1965, 0.3591, 0.1920, 0.3276, 0.1955, 0.1802, 0.2144, 0.3389, 0.3288,\n",
-       "         0.2844, 0.2093, 0.4740, 0.1968, 0.1822, 0.2822, 0.2631, 0.1810, 0.2247,\n",
-       "         0.1525, 0.1969, 0.2946, 0.2421, 0.3753, 0.3834, 0.2667, 0.2303, 0.1842,\n",
-       "         0.1955, 0.2255, 0.2135, 0.2413, 0.2960, 0.2542, 0.2231, 0.1362, 0.1236,\n",
-       "         0.2096, 0.2362, 0.3448, 0.2774, 0.1985, 0.2707, 0.2330, 0.2973, 0.1932,\n",
-       "         0.2438, 0.1668, 0.3396, 0.3164, 0.4281, 0.4154, 0.2543, 0.2125, 0.2177,\n",
-       "         0.2048, 0.3659, 0.3149, 0.3590, 0.3374, 0.1791, 0.2422, 0.2422, 0.2388,\n",
-       "         0.2000], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.0648, -0.2535,  0.1674,  0.0280, -0.0821,  0.0078,  0.0331, -0.0859,\n",
-       "         -0.1087, -0.0736, -0.1206, -0.2986,  0.0566,  0.0953, -0.0478, -0.1337,\n",
-       "          0.0317, -0.0176,  0.0713,  0.1268, -0.1719,  0.0692, -0.2256, -0.3553,\n",
-       "          0.0116, -0.0608,  0.0513, -0.0470,  0.1242, -0.0573, -0.0510, -0.2359,\n",
-       "         -0.2733, -0.0307,  0.1591,  0.3395, -0.0189,  0.0755, -0.1732, -0.1946,\n",
-       "         -0.0277, -0.1042,  0.0515, -0.3030,  0.0144, -0.2752,  0.1541, -0.2550,\n",
-       "         -0.2141, -0.2933, -0.3406, -0.2363,  0.1185,  0.0257,  0.1633, -0.2754,\n",
-       "         -0.3160, -0.3122, -0.2488,  0.0990,  0.0264,  0.1139, -0.0690,  0.0317],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-0.0141, -0.0641, -0.0308],\n",
-       "           [ 0.0261,  0.0915,  0.0077],\n",
-       "           [ 0.0182, -0.0148,  0.0577]],\n",
-       " \n",
-       "          [[-0.0167, -0.0037, -0.0239],\n",
-       "           [-0.0057,  0.0239,  0.0180],\n",
-       "           [-0.0231, -0.0414,  0.0224]],\n",
-       " \n",
-       "          [[-0.0557, -0.0383, -0.0712],\n",
-       "           [-0.0704, -0.0551, -0.0267],\n",
-       "           [-0.0484, -0.0448, -0.0059]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0235,  0.0365, -0.0479],\n",
-       "           [ 0.0227,  0.0379,  0.0125],\n",
-       "           [-0.0126, -0.0002,  0.0155]],\n",
-       " \n",
-       "          [[-0.0338, -0.0187, -0.0118],\n",
-       "           [ 0.0131, -0.0080, -0.0057],\n",
-       "           [ 0.0461, -0.1180,  0.0575]],\n",
-       " \n",
-       "          [[-0.0031, -0.0470, -0.0555],\n",
-       "           [-0.0021,  0.0023, -0.0374],\n",
-       "           [-0.0002,  0.0020,  0.0169]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0520, -0.0648, -0.0578],\n",
-       "           [-0.0455, -0.0189, -0.0413],\n",
-       "           [-0.0314,  0.0031, -0.0213]],\n",
-       " \n",
-       "          [[-0.0311, -0.0435, -0.0219],\n",
-       "           [-0.0761, -0.0858, -0.0215],\n",
-       "           [-0.0618, -0.0493,  0.0179]],\n",
-       " \n",
-       "          [[ 0.0050, -0.0125, -0.0149],\n",
-       "           [ 0.0086, -0.0008, -0.0035],\n",
-       "           [-0.0296, -0.0349, -0.0560]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0401,  0.0158, -0.0153],\n",
-       "           [-0.0524,  0.0348, -0.0265],\n",
-       "           [-0.0637, -0.0193, -0.0377]],\n",
-       " \n",
-       "          [[ 0.0027,  0.0047,  0.0174],\n",
-       "           [ 0.0014,  0.0135, -0.0076],\n",
-       "           [-0.0064,  0.0258,  0.0431]],\n",
-       " \n",
-       "          [[-0.0216,  0.0034, -0.0369],\n",
-       "           [ 0.0175,  0.0071, -0.0326],\n",
-       "           [ 0.0266,  0.0203, -0.0274]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0802,  0.0137,  0.0504],\n",
-       "           [ 0.0280,  0.0444,  0.0343],\n",
-       "           [ 0.0022,  0.1039,  0.0007]],\n",
-       " \n",
-       "          [[-0.0386, -0.0501, -0.0082],\n",
-       "           [-0.0172, -0.0467, -0.0629],\n",
-       "           [-0.0031, -0.0269, -0.0532]],\n",
-       " \n",
-       "          [[ 0.0969, -0.0353,  0.0545],\n",
-       "           [-0.0078, -0.1803, -0.0178],\n",
-       "           [ 0.1301,  0.0796,  0.0532]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0108,  0.0304,  0.0275],\n",
-       "           [-0.0234,  0.0245, -0.0126],\n",
-       "           [-0.0048,  0.0188,  0.0293]],\n",
-       " \n",
-       "          [[ 0.0067, -0.0263, -0.0162],\n",
-       "           [ 0.0216, -0.0966,  0.0460],\n",
-       "           [ 0.0287, -0.0125, -0.0340]],\n",
-       " \n",
-       "          [[ 0.0163,  0.0351,  0.0223],\n",
-       "           [-0.0318,  0.0157,  0.0147],\n",
-       "           [-0.0027,  0.0066, -0.0085]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0155,  0.0161,  0.0269],\n",
-       "           [ 0.0088,  0.0039,  0.0222],\n",
-       "           [-0.0152, -0.0023, -0.0171]],\n",
-       " \n",
-       "          [[-0.0302, -0.0195, -0.0046],\n",
-       "           [-0.0343, -0.0419, -0.0250],\n",
-       "           [-0.0192, -0.0226, -0.0128]],\n",
-       " \n",
-       "          [[-0.0231, -0.0515, -0.0084],\n",
-       "           [-0.0681, -0.0974, -0.0252],\n",
-       "           [-0.0099, -0.0241, -0.0046]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0308, -0.0286, -0.0091],\n",
-       "           [-0.0235, -0.0283, -0.0141],\n",
-       "           [-0.0308, -0.0270, -0.0015]],\n",
-       " \n",
-       "          [[ 0.0137, -0.0063,  0.0026],\n",
-       "           [ 0.0209, -0.0303, -0.0125],\n",
-       "           [ 0.0152, -0.0288, -0.0195]],\n",
-       " \n",
-       "          [[-0.0188, -0.0375, -0.0236],\n",
-       "           [-0.0283, -0.0546, -0.0300],\n",
-       "           [-0.0128, -0.0236, -0.0144]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0242, -0.0069, -0.0428],\n",
-       "           [-0.0851, -0.0989,  0.0041],\n",
-       "           [-0.0216, -0.0288, -0.0799]],\n",
-       " \n",
-       "          [[-0.0135, -0.0050,  0.0250],\n",
-       "           [ 0.0395,  0.0828, -0.0474],\n",
-       "           [ 0.0264, -0.0553, -0.0264]],\n",
-       " \n",
-       "          [[-0.0023, -0.0352, -0.0825],\n",
-       "           [-0.0248,  0.0935, -0.1404],\n",
-       "           [-0.1181, -0.0212,  0.0539]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0381,  0.0688, -0.0514],\n",
-       "           [-0.0245, -0.0429, -0.0278],\n",
-       "           [ 0.0071, -0.0148, -0.0177]],\n",
-       " \n",
-       "          [[ 0.0140,  0.0249,  0.0381],\n",
-       "           [ 0.0089,  0.0643, -0.0712],\n",
-       "           [ 0.0018,  0.0768,  0.0198]],\n",
-       " \n",
-       "          [[ 0.0102,  0.0132,  0.0020],\n",
-       "           [ 0.0331, -0.1049,  0.0117],\n",
-       "           [ 0.0116,  0.0057,  0.0216]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0145,  0.0080, -0.0097],\n",
-       "           [ 0.0089, -0.0117, -0.0356],\n",
-       "           [-0.0296, -0.0088, -0.0327]],\n",
-       " \n",
-       "          [[ 0.0426,  0.0374,  0.0417],\n",
-       "           [ 0.0536,  0.0433,  0.0431],\n",
-       "           [ 0.0337,  0.0244,  0.0317]],\n",
-       " \n",
-       "          [[ 0.0130,  0.0160,  0.0207],\n",
-       "           [ 0.0167,  0.0268, -0.0123],\n",
-       "           [-0.0208, -0.0028, -0.0144]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0304,  0.0312,  0.0505],\n",
-       "           [ 0.0265,  0.0393,  0.0643],\n",
-       "           [ 0.0274,  0.0363,  0.0441]],\n",
-       " \n",
-       "          [[-0.0543,  0.0079,  0.0018],\n",
-       "           [ 0.0109,  0.1054, -0.0256],\n",
-       "           [ 0.0014,  0.0314, -0.0279]],\n",
-       " \n",
-       "          [[ 0.0600,  0.0210,  0.0091],\n",
-       "           [ 0.0653,  0.0251,  0.0090],\n",
-       "           [ 0.0203,  0.0210, -0.0182]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.4262, 0.2112, 0.2378, 0.1528, 0.4145, 0.0480, 0.3833, 0.1098, 0.1308,\n",
-       "         0.1857, 0.0722, 0.2417, 0.1119, 0.3324, 0.2981, 0.3844, 0.5271, 0.1870,\n",
-       "         0.3430, 0.2351, 0.1828, 0.3477, 0.3087, 0.5319, 0.1166, 0.3845, 0.3127,\n",
-       "         0.2154, 0.2634, 0.2149, 0.1589, 0.2457, 0.2965, 0.4988, 0.1525, 0.2258,\n",
-       "         0.1446, 0.2295, 0.2164, 0.1735, 0.3035, 0.3836, 0.1493, 0.2250, 0.3204,\n",
-       "         0.3245, 0.3382, 0.2908, 0.1996, 0.2842, 0.2179, 0.2456, 0.3433, 0.2889,\n",
-       "         0.1217, 0.1404, 0.2811, 0.3134, 0.2115, 0.2618, 0.3669, 0.1072, 0.3369,\n",
-       "         0.3374], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.0031, -0.0600,  0.1502, -0.0909,  0.2021, -0.0741, -0.0868, -0.1455,\n",
-       "         -0.3606, -0.0055, -0.0042, -0.1470,  0.0638,  0.2198,  0.0720,  0.1231,\n",
-       "          0.0015, -0.1126,  0.0148, -0.0182, -0.0202,  0.1622,  0.2340,  0.1168,\n",
-       "          0.0106, -0.0385,  0.0064, -0.0599,  0.0617, -0.2602, -0.0598,  0.0364,\n",
-       "         -0.1566, -0.2414,  0.1455, -0.1210,  0.0647,  0.0243, -0.0266, -0.1157,\n",
-       "         -0.0191,  0.2281,  0.1651, -0.0111,  0.1616,  0.2220,  0.2582,  0.0529,\n",
-       "         -0.1774,  0.1196,  0.1100, -0.0875,  0.2118, -0.0954, -0.0686,  0.0909,\n",
-       "          0.2527,  0.0399, -0.1552, -0.1740,  0.2111, -0.0425,  0.3537, -0.0125],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 1.9080e-02, -1.0406e-01, -2.4292e-02],\n",
-       "           [ 1.4246e-02,  1.1254e-01,  3.4832e-02],\n",
-       "           [-2.6855e-02, -8.4238e-03, -3.5984e-02]],\n",
-       " \n",
-       "          [[-7.0985e-02, -1.3652e-02, -2.5403e-02],\n",
-       "           [-2.8650e-02,  6.1509e-02, -2.0224e-02],\n",
-       "           [-6.7613e-02,  1.0391e-02, -2.4732e-02]],\n",
-       " \n",
-       "          [[-1.7074e-02, -2.1289e-02, -4.8826e-02],\n",
-       "           [-4.4577e-02,  2.9041e-02,  3.0170e-03],\n",
-       "           [ 1.0579e-02, -1.5271e-03, -2.4705e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-3.0153e-02,  1.4140e-02, -3.0884e-02],\n",
-       "           [-5.0899e-02,  1.8717e-02, -3.3920e-02],\n",
-       "           [-3.0025e-02,  5.9867e-03, -4.2500e-02]],\n",
-       " \n",
-       "          [[-1.4855e-02, -7.6980e-03, -7.2206e-02],\n",
-       "           [ 4.1705e-02, -7.9349e-02, -1.7984e-02],\n",
-       "           [-4.1706e-02, -4.4115e-02, -6.0107e-02]],\n",
-       " \n",
-       "          [[ 3.0779e-02, -7.9644e-03, -1.2198e-02],\n",
-       "           [ 6.8021e-02,  2.2908e-02,  1.5591e-02],\n",
-       "           [ 2.2507e-02,  5.6491e-03,  1.2283e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-3.0154e-02,  1.0541e-02,  1.0326e-01],\n",
-       "           [ 2.0122e-02, -2.2904e-02,  8.0565e-03],\n",
-       "           [ 5.2730e-02,  4.2792e-02,  7.7651e-02]],\n",
-       " \n",
-       "          [[ 2.0934e-03, -3.1179e-02,  4.1413e-02],\n",
-       "           [ 5.3052e-03, -3.7664e-02,  2.2774e-02],\n",
-       "           [ 1.6808e-02, -3.9837e-02,  3.6500e-03]],\n",
-       " \n",
-       "          [[-2.6250e-02, -2.2517e-02, -4.7567e-02],\n",
-       "           [-5.0860e-02, -2.5106e-02,  7.9052e-03],\n",
-       "           [ 8.4075e-03, -3.4406e-02, -2.0514e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.0978e-02, -3.3373e-02, -5.3792e-03],\n",
-       "           [ 4.7342e-02, -4.8457e-02, -2.2072e-03],\n",
-       "           [ 5.0815e-03, -3.9025e-02, -2.6393e-02]],\n",
-       " \n",
-       "          [[-5.3040e-03, -7.7148e-03,  8.1520e-03],\n",
-       "           [-5.9797e-03,  1.0135e-01,  4.3636e-02],\n",
-       "           [-2.2621e-03,  4.6048e-02,  3.1871e-02]],\n",
-       " \n",
-       "          [[-1.5185e-02, -1.0902e-02, -1.5799e-02],\n",
-       "           [-1.4897e-04, -5.8247e-04, -2.5043e-02],\n",
-       "           [-3.2683e-03, -7.7904e-04, -3.0475e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 2.1672e-02,  3.1621e-02,  4.1312e-02],\n",
-       "           [-1.2713e-02, -9.3995e-03,  1.9467e-02],\n",
-       "           [-3.6951e-02, -1.7693e-02, -1.3301e-02]],\n",
-       " \n",
-       "          [[-3.4305e-03,  1.7646e-02, -1.8492e-03],\n",
-       "           [-6.4864e-03,  7.5278e-03, -2.1195e-02],\n",
-       "           [-2.5285e-03,  9.5994e-03, -3.9323e-03]],\n",
-       " \n",
-       "          [[ 8.9804e-03,  8.8679e-03, -1.1616e-02],\n",
-       "           [ 1.4058e-02,  3.4678e-02,  1.0567e-02],\n",
-       "           [-1.0747e-02,  5.1668e-03, -4.0235e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.8487e-02,  2.3031e-02,  1.3480e-02],\n",
-       "           [ 1.5028e-02,  1.3475e-02, -2.5213e-03],\n",
-       "           [ 3.9184e-05,  2.2632e-02,  8.1728e-03]],\n",
-       " \n",
-       "          [[ 2.5119e-02,  4.8904e-02,  4.7781e-02],\n",
-       "           [ 5.2553e-02,  6.7240e-03, -2.7629e-02],\n",
-       "           [ 6.4616e-02,  2.1688e-02, -5.6018e-03]],\n",
-       " \n",
-       "          [[ 1.5091e-02,  2.5635e-02,  2.7092e-02],\n",
-       "           [-4.9328e-03, -2.9643e-03, -3.7165e-04],\n",
-       "           [-1.2832e-02, -1.6589e-02, -1.9600e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-4.6861e-02, -1.0521e-02,  5.0368e-02],\n",
-       "           [ 1.3948e-02, -9.5916e-03,  6.1983e-02],\n",
-       "           [ 4.0443e-02, -9.0842e-03, -2.6106e-03]],\n",
-       " \n",
-       "          [[ 2.2668e-02, -3.0250e-02, -1.6415e-03],\n",
-       "           [ 1.2831e-02, -4.7060e-02, -4.6135e-02],\n",
-       "           [-2.1458e-02, -4.8901e-02, -7.9821e-02]],\n",
-       " \n",
-       "          [[-6.1582e-02, -3.4639e-02, -7.1583e-03],\n",
-       "           [-1.2078e-02,  1.0040e-02,  2.0845e-03],\n",
-       "           [ 1.3535e-02,  4.6558e-02,  5.6382e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.7613e-02, -3.5253e-02, -9.1764e-03],\n",
-       "           [ 1.2211e-02,  4.4089e-02,  1.7582e-02],\n",
-       "           [-7.7208e-03,  4.4501e-02,  3.3717e-02]],\n",
-       " \n",
-       "          [[-2.6456e-02,  1.1835e-02,  1.8335e-04],\n",
-       "           [-3.7534e-02,  1.0787e-02,  4.1256e-02],\n",
-       "           [-2.6080e-03, -1.9320e-02,  7.4794e-02]],\n",
-       " \n",
-       "          [[ 2.0881e-02,  1.3242e-03, -1.1274e-02],\n",
-       "           [ 4.2437e-02,  4.4695e-02,  1.9202e-02],\n",
-       "           [ 2.7355e-02,  3.5607e-02,  2.5424e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-2.7262e-02, -2.2114e-02,  2.8495e-02],\n",
-       "           [-5.3803e-02, -8.0432e-02, -5.9061e-04],\n",
-       "           [-3.6788e-02, -2.5028e-02,  2.2270e-02]],\n",
-       " \n",
-       "          [[-1.0353e-02, -9.3990e-03, -1.8485e-02],\n",
-       "           [-2.4481e-02,  2.5071e-02, -2.3113e-02],\n",
-       "           [ 1.3373e-02,  5.3689e-02,  3.1985e-02]],\n",
-       " \n",
-       "          [[ 2.0001e-02, -7.3912e-02, -5.7653e-02],\n",
-       "           [ 1.1499e-01,  1.1340e-01,  9.4085e-03],\n",
-       "           [ 6.4868e-03,  6.2206e-02, -8.1177e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.8227e-03, -9.3895e-03, -2.6817e-02],\n",
-       "           [ 7.1001e-03,  1.1446e-01,  1.4609e-02],\n",
-       "           [-5.4445e-02, -5.5009e-03, -4.5246e-02]],\n",
-       " \n",
-       "          [[-2.2011e-02,  8.3600e-02,  1.1213e-01],\n",
-       "           [ 1.0447e-02, -1.8065e-01,  1.8462e-02],\n",
-       "           [ 8.4509e-02, -6.7443e-02,  5.7314e-02]],\n",
-       " \n",
-       "          [[ 4.0990e-02,  2.7187e-02,  2.1823e-02],\n",
-       "           [ 2.8184e-02,  4.9923e-03,  4.7683e-03],\n",
-       "           [ 5.7597e-02,  3.7237e-02,  6.0262e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.4146e-02, -3.4709e-02,  7.7762e-03],\n",
-       "           [ 3.5869e-02, -2.1530e-02,  1.7095e-02],\n",
-       "           [ 2.0974e-02, -3.5630e-02, -2.8340e-03]],\n",
-       " \n",
-       "          [[-1.6405e-02, -2.4550e-02,  2.3571e-02],\n",
-       "           [-2.1341e-02, -2.0764e-02,  3.4690e-02],\n",
-       "           [-2.1494e-02, -8.7241e-03,  3.5134e-02]],\n",
-       " \n",
-       "          [[-6.0019e-02, -6.0631e-02, -9.3567e-04],\n",
-       "           [-2.0145e-02, -4.6441e-02,  2.3322e-02],\n",
-       "           [-1.9610e-02, -5.8794e-02, -2.1558e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.7234e-02, -8.4518e-03,  2.1283e-03],\n",
-       "           [ 8.6251e-03, -8.4881e-03,  8.7093e-03],\n",
-       "           [-8.8363e-03,  1.2403e-02,  3.4956e-02]],\n",
-       " \n",
-       "          [[ 2.8412e-03, -4.1221e-03, -2.5370e-02],\n",
-       "           [ 6.0771e-02,  9.3556e-03,  1.4630e-02],\n",
-       "           [ 1.0755e-03, -7.1570e-02,  2.6690e-02]],\n",
-       " \n",
-       "          [[ 2.5778e-02, -4.7832e-03, -7.7243e-03],\n",
-       "           [ 4.2013e-02,  8.7470e-03,  7.9601e-03],\n",
-       "           [ 3.8801e-02,  3.7465e-03, -1.1508e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.3483, 0.2026, 0.2187, 0.2603, 0.2582, 0.1942, 0.1325, 0.2548, 0.2510,\n",
-       "         0.2621, 0.2129, 0.2926, 0.2950, 0.3142, 0.3836, 0.2493, 0.2881, 0.1815,\n",
-       "         0.2898, 0.3297, 0.3134, 0.1681, 0.3032, 0.2473, 0.2805, 0.2686, 0.2741,\n",
-       "         0.2801, 0.2956, 0.2170, 0.2646, 0.3064, 0.1983, 0.2902, 0.2896, 0.3537,\n",
-       "         0.1879, 0.3257, 0.2948, 0.2899, 0.2248, 0.3157, 0.2102, 0.2432, 0.2569,\n",
-       "         0.2417, 0.3135, 0.2879, 0.2274, 0.2395, 0.2280, 0.3093, 0.2938, 0.2744,\n",
-       "         0.2582, 0.3011, 0.1924, 0.2220, 0.2733, 0.2062, 0.2669, 0.2358, 0.3063,\n",
-       "         0.2864], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.2542, -0.0251, -0.0688, -0.2270,  0.0330,  0.1011,  0.1641, -0.0960,\n",
-       "         -0.1400, -0.1575,  0.0537, -0.0735, -0.2277, -0.0977, -0.2484, -0.1241,\n",
-       "         -0.1393,  0.1411, -0.1893, -0.2405, -0.2460,  0.0914, -0.1769,  0.0957,\n",
-       "         -0.1848, -0.2498, -0.1634, -0.2164, -0.2435, -0.0260, -0.1856, -0.3146,\n",
-       "         -0.1397, -0.1004, -0.1152, -0.4198, -0.0679, -0.1583, -0.1929, -0.2534,\n",
-       "          0.0349, -0.2496, -0.0418, -0.0574, -0.2722, -0.2788, -0.1952, -0.1513,\n",
-       "         -0.3203, -0.1279, -0.1139, -0.1999, -0.1324, -0.1592,  0.0253, -0.0050,\n",
-       "          0.0245,  0.1097, -0.0325, -0.0045, -0.0170, -0.1213, -0.3592, -0.1694],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-0.0222,  0.0036, -0.0362],\n",
-       "           [-0.0345,  0.0357, -0.0427],\n",
-       "           [-0.0124, -0.0932, -0.0325]],\n",
-       " \n",
-       "          [[-0.0136,  0.0468, -0.0065],\n",
-       "           [-0.0329,  0.0008,  0.0133],\n",
-       "           [ 0.0554, -0.0488,  0.0240]],\n",
-       " \n",
-       "          [[-0.0002, -0.0238, -0.0036],\n",
-       "           [-0.0223, -0.0468,  0.0012],\n",
-       "           [-0.0216, -0.0303,  0.0110]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0316,  0.0123, -0.0859],\n",
-       "           [ 0.0891,  0.0817, -0.0527],\n",
-       "           [ 0.0277,  0.0028, -0.0783]],\n",
-       " \n",
-       "          [[-0.0078, -0.0139, -0.0013],\n",
-       "           [ 0.0175,  0.0056, -0.0197],\n",
-       "           [ 0.0087, -0.0504,  0.0175]],\n",
-       " \n",
-       "          [[-0.0203, -0.0198, -0.0294],\n",
-       "           [-0.0315, -0.0437, -0.0365],\n",
-       "           [-0.0076, -0.0315, -0.0251]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0054, -0.0193,  0.0002],\n",
-       "           [ 0.0088,  0.0116,  0.0334],\n",
-       "           [ 0.0264,  0.0292,  0.0554]],\n",
-       " \n",
-       "          [[-0.0140, -0.0031,  0.0055],\n",
-       "           [-0.0311,  0.0005,  0.0083],\n",
-       "           [-0.0419,  0.0013,  0.0147]],\n",
-       " \n",
-       "          [[-0.0011, -0.0009,  0.0282],\n",
-       "           [ 0.0246,  0.0162,  0.0302],\n",
-       "           [ 0.0421,  0.0346,  0.0275]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0125,  0.0329,  0.0349],\n",
-       "           [-0.0156,  0.0018,  0.0518],\n",
-       "           [ 0.0336,  0.0189,  0.0548]],\n",
-       " \n",
-       "          [[-0.0423, -0.0763, -0.0875],\n",
-       "           [-0.0572, -0.0979, -0.0892],\n",
-       "           [-0.0365, -0.0520, -0.0341]],\n",
-       " \n",
-       "          [[-0.0180, -0.0136,  0.0029],\n",
-       "           [-0.0017, -0.0101,  0.0030],\n",
-       "           [-0.0081, -0.0056,  0.0039]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0073, -0.0128,  0.0352],\n",
-       "           [ 0.0149, -0.0055,  0.0015],\n",
-       "           [-0.0349, -0.0017, -0.0049]],\n",
-       " \n",
-       "          [[-0.0124, -0.0077,  0.0008],\n",
-       "           [-0.0392, -0.0118, -0.0090],\n",
-       "           [-0.0139, -0.0470, -0.0358]],\n",
-       " \n",
-       "          [[-0.0242, -0.0306, -0.0236],\n",
-       "           [-0.0118, -0.0391, -0.0343],\n",
-       "           [-0.0078, -0.0340, -0.0298]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0174, -0.0085,  0.0401],\n",
-       "           [ 0.0098, -0.0309, -0.0025],\n",
-       "           [-0.0081,  0.0108, -0.0284]],\n",
-       " \n",
-       "          [[-0.0425, -0.1625, -0.1118],\n",
-       "           [-0.0307, -0.0730, -0.0382],\n",
-       "           [ 0.0121, -0.0055, -0.0333]],\n",
-       " \n",
-       "          [[-0.0095,  0.0330,  0.0041],\n",
-       "           [-0.0147, -0.0152,  0.0144],\n",
-       "           [ 0.0156,  0.0488,  0.0236]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-0.0187,  0.0049, -0.0127],\n",
-       "           [-0.0006,  0.0070, -0.0019],\n",
-       "           [-0.0025,  0.0103, -0.0156]],\n",
-       " \n",
-       "          [[-0.0408, -0.0359, -0.0345],\n",
-       "           [-0.0300, -0.0332, -0.0227],\n",
-       "           [-0.0587, -0.0298, -0.0247]],\n",
-       " \n",
-       "          [[-0.0483, -0.0488, -0.0264],\n",
-       "           [-0.0486, -0.0515, -0.0310],\n",
-       "           [-0.0272, -0.0271, -0.0092]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0370,  0.0554,  0.0005],\n",
-       "           [ 0.0203,  0.0352,  0.0213],\n",
-       "           [ 0.0012, -0.0073, -0.0216]],\n",
-       " \n",
-       "          [[-0.0616, -0.1264, -0.0732],\n",
-       "           [-0.0837, -0.0529, -0.0541],\n",
-       "           [-0.0305, -0.0297, -0.0316]],\n",
-       " \n",
-       "          [[-0.0266, -0.0083,  0.0033],\n",
-       "           [ 0.0028, -0.0027, -0.0097],\n",
-       "           [-0.0043,  0.0016, -0.0146]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0237, -0.0387,  0.0158],\n",
-       "           [-0.0149, -0.0376,  0.0081],\n",
-       "           [-0.0181, -0.0437, -0.0196]],\n",
-       " \n",
-       "          [[-0.0102, -0.0206, -0.0038],\n",
-       "           [-0.0137, -0.0893,  0.0389],\n",
-       "           [-0.0061,  0.0091,  0.0252]],\n",
-       " \n",
-       "          [[-0.0348, -0.0447, -0.0578],\n",
-       "           [-0.0047, -0.0133, -0.0448],\n",
-       "           [-0.0794, -0.0521, -0.0193]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0810, -0.1184,  0.0049],\n",
-       "           [-0.0115, -0.0179, -0.0356],\n",
-       "           [ 0.0112,  0.0256, -0.0336]],\n",
-       " \n",
-       "          [[ 0.0623,  0.0298, -0.0780],\n",
-       "           [-0.0058, -0.0666,  0.0493],\n",
-       "           [-0.0326,  0.0382,  0.0252]],\n",
-       " \n",
-       "          [[ 0.0152,  0.0137, -0.0132],\n",
-       "           [-0.0323, -0.0803, -0.0279],\n",
-       "           [ 0.0268,  0.0252, -0.0560]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0484,  0.0222,  0.0523],\n",
-       "           [ 0.0203,  0.0303,  0.0457],\n",
-       "           [-0.0053, -0.0033,  0.0185]],\n",
-       " \n",
-       "          [[-0.0290, -0.0404, -0.0524],\n",
-       "           [-0.0148, -0.0083, -0.0114],\n",
-       "           [-0.0423, -0.0111, -0.0556]],\n",
-       " \n",
-       "          [[-0.0286, -0.0164, -0.0352],\n",
-       "           [-0.0380, -0.0293, -0.0287],\n",
-       "           [-0.0460, -0.0257, -0.0434]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0032, -0.0120, -0.0535],\n",
-       "           [-0.0242, -0.0433, -0.0345],\n",
-       "           [-0.0438, -0.0451, -0.0260]],\n",
-       " \n",
-       "          [[ 0.0264,  0.0181, -0.0016],\n",
-       "           [ 0.0042,  0.0119,  0.0063],\n",
-       "           [ 0.0138,  0.0035, -0.0327]],\n",
-       " \n",
-       "          [[ 0.0237,  0.0127,  0.0072],\n",
-       "           [ 0.0239,  0.0160,  0.0236],\n",
-       "           [ 0.0092, -0.0117,  0.0002]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.5589,  0.1928,  0.2910,  0.1713,  0.2596,  0.1905,  0.3369,  0.0425,\n",
-       "         -0.0076,  0.0648,  0.2034,  0.2441,  0.0270,  0.4198,  0.1700,  0.2120,\n",
-       "          0.5019,  0.2450,  0.4431,  0.1497,  0.1748,  0.3593,  0.3071,  0.3874,\n",
-       "         -0.0071,  0.3584,  0.3681,  0.1580,  0.3323,  0.2573,  0.2054,  0.1865,\n",
-       "          0.3076,  0.4107,  0.1326,  0.3174,  0.0462,  0.2896,  0.1205,  0.1876,\n",
-       "          0.3517,  0.4347,  0.2640,  0.1328,  0.4189,  0.3648,  0.4076,  0.4332,\n",
-       "          0.4387,  0.1477,  0.1153,  0.1830,  0.3734,  0.1976,  0.1503, -0.0132,\n",
-       "          0.2767,  0.0850,  0.1542,  0.1364,  0.4255,  0.1796,  0.3355,  0.2374],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.0887, -0.1848,  0.0710,  0.0554,  0.1052,  0.1908, -0.0857, -0.0986,\n",
-       "          0.0359, -0.0087, -0.1041,  0.0363,  0.0124,  0.0906,  0.0142,  0.1757,\n",
-       "         -0.0202, -0.1185, -0.1759, -0.0167,  0.0653,  0.0235,  0.0620,  0.0876,\n",
-       "         -0.0263,  0.2988, -0.0042,  0.1051,  0.0547,  0.1525,  0.0449,  0.0371,\n",
-       "         -0.1787,  0.1951,  0.1882,  0.0060,  0.0276, -0.0043,  0.0151, -0.2045,\n",
-       "          0.0460,  0.1798,  0.1675,  0.0227, -0.0041,  0.1486,  0.0819,  0.0680,\n",
-       "         -0.0781,  0.0531,  0.0878, -0.0804, -0.0586, -0.0143,  0.0596, -0.0270,\n",
-       "          0.1236,  0.1507,  0.1014, -0.1233,  0.0542,  0.2096,  0.0858, -0.0079],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-4.8811e-03,  3.8080e-03,  4.6741e-03],\n",
-       "           [-1.8355e-02,  4.3191e-02, -4.5375e-02],\n",
-       "           [-2.7688e-02,  1.5644e-02, -1.3964e-02]],\n",
-       " \n",
-       "          [[-1.2956e-02, -6.5772e-03,  3.7120e-03],\n",
-       "           [-1.1408e-02,  6.2519e-03,  5.8859e-03],\n",
-       "           [-4.0796e-02, -1.4412e-02, -8.2872e-03]],\n",
-       " \n",
-       "          [[-2.0156e-03,  3.9541e-02, -4.0419e-02],\n",
-       "           [-4.9482e-02,  5.4714e-02, -3.0851e-02],\n",
-       "           [-7.0856e-02,  2.3893e-02, -2.5945e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.5611e-02, -1.4751e-02, -2.1564e-02],\n",
-       "           [-3.2966e-02, -3.2014e-02, -1.5926e-02],\n",
-       "           [ 5.7803e-03, -1.3064e-02,  1.0837e-03]],\n",
-       " \n",
-       "          [[ 5.2602e-04,  2.0849e-02, -1.3532e-02],\n",
-       "           [ 6.8543e-02, -1.2897e-05,  3.5734e-02],\n",
-       "           [ 6.7066e-02,  3.5616e-03,  3.2268e-02]],\n",
-       " \n",
-       "          [[-3.7049e-02, -2.5910e-02,  6.9538e-03],\n",
-       "           [-4.7130e-02, -6.5635e-02,  1.0382e-02],\n",
-       "           [-3.8169e-02, -7.8627e-02,  7.5256e-04]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 4.1118e-02,  1.2052e-01,  1.1652e-01],\n",
-       "           [ 8.3001e-04, -1.0855e-02, -7.3706e-02],\n",
-       "           [-2.0400e-02, -7.5544e-03,  1.4089e-02]],\n",
-       " \n",
-       "          [[-6.0763e-03,  3.0219e-03, -2.4969e-03],\n",
-       "           [-2.5690e-02, -3.6784e-02, -3.4722e-02],\n",
-       "           [-2.5027e-02, -3.8288e-02, -1.7522e-02]],\n",
-       " \n",
-       "          [[ 3.7955e-02,  3.0817e-02,  3.9281e-02],\n",
-       "           [ 2.8651e-02,  4.0591e-02,  1.7896e-02],\n",
-       "           [ 7.0577e-02,  6.0905e-02,  2.0952e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.4662e-02,  1.8776e-02, -3.3473e-03],\n",
-       "           [ 1.4922e-02,  2.7669e-03, -2.9453e-02],\n",
-       "           [ 7.8285e-03, -1.7945e-02,  4.7844e-02]],\n",
-       " \n",
-       "          [[ 2.6036e-02,  4.3112e-02,  1.0713e-03],\n",
-       "           [-9.0296e-02, -1.3542e-01, -7.3473e-02],\n",
-       "           [-7.2666e-03,  3.9876e-02,  1.1096e-02]],\n",
-       " \n",
-       "          [[ 1.2818e-01,  7.1388e-02,  6.3807e-02],\n",
-       "           [ 9.2664e-03, -4.9611e-02, -1.8900e-02],\n",
-       "           [ 4.6117e-02,  1.8852e-02,  5.3599e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 2.4433e-02,  4.4335e-03, -5.3387e-03],\n",
-       "           [-1.7485e-03,  1.0016e-02, -1.1116e-02],\n",
-       "           [-2.6334e-02, -8.8572e-04,  2.5744e-02]],\n",
-       " \n",
-       "          [[ 2.3269e-02,  1.8728e-03, -5.4677e-03],\n",
-       "           [ 3.0843e-02,  1.9419e-02, -1.8846e-02],\n",
-       "           [ 3.5182e-02,  4.2719e-02,  1.7611e-02]],\n",
-       " \n",
-       "          [[-1.2791e-02,  6.3720e-04,  8.0341e-03],\n",
-       "           [-1.3429e-02,  2.5620e-02,  5.7284e-02],\n",
-       "           [ 3.3626e-03, -5.1158e-03,  2.1024e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.3176e-02,  2.7455e-02,  2.9702e-02],\n",
-       "           [-2.7254e-02, -4.1524e-03,  3.6514e-02],\n",
-       "           [-6.0157e-02, -2.3370e-02, -2.1842e-02]],\n",
-       " \n",
-       "          [[-1.1310e-02, -9.5904e-03,  5.4431e-02],\n",
-       "           [-4.8535e-02, -2.6822e-03,  2.2237e-02],\n",
-       "           [-8.3234e-03, -1.7544e-02,  2.5246e-03]],\n",
-       " \n",
-       "          [[ 3.0313e-02,  3.9132e-02,  5.4808e-02],\n",
-       "           [ 1.2379e-02,  2.7511e-02,  3.8434e-02],\n",
-       "           [ 4.2326e-02,  5.0218e-02,  3.6970e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-3.5016e-02, -1.5987e-02, -4.6694e-02],\n",
-       "           [-1.7885e-02,  2.5631e-02, -5.0116e-02],\n",
-       "           [-2.0520e-02, -3.1743e-02, -5.1765e-02]],\n",
-       " \n",
-       "          [[ 3.2083e-02, -2.3953e-02,  1.2974e-02],\n",
-       "           [ 1.5669e-02, -4.2657e-02,  2.5186e-03],\n",
-       "           [-3.4647e-02, -2.5478e-02, -2.1361e-02]],\n",
-       " \n",
-       "          [[-7.5085e-02,  6.7877e-03,  5.7943e-02],\n",
-       "           [ 6.7115e-02, -1.6920e-01,  1.1554e-01],\n",
-       "           [ 1.1420e-01, -3.1395e-02, -4.2631e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 6.2097e-02, -3.8645e-02,  2.9584e-02],\n",
-       "           [ 7.4566e-02, -1.6224e-02, -4.1155e-02],\n",
-       "           [-1.6289e-02,  8.2787e-02, -3.2636e-02]],\n",
-       " \n",
-       "          [[-2.2942e-02,  1.1838e-01, -1.1599e-01],\n",
-       "           [-1.7032e-01,  9.1357e-02,  2.1345e-02],\n",
-       "           [ 4.2437e-02, -1.2197e-01,  1.3909e-01]],\n",
-       " \n",
-       "          [[ 4.3302e-03,  3.5805e-02,  1.4536e-02],\n",
-       "           [-1.4381e-02,  6.4032e-03,  8.3829e-03],\n",
-       "           [ 5.9500e-03,  1.1023e-02,  5.9301e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-5.3951e-02, -8.7259e-02, -5.9768e-02],\n",
-       "           [-5.0183e-02, -6.1384e-02, -2.2933e-02],\n",
-       "           [ 9.9610e-03,  3.7695e-04,  1.2552e-02]],\n",
-       " \n",
-       "          [[-1.8440e-02, -5.2279e-03,  1.6004e-02],\n",
-       "           [-1.0563e-02,  3.8663e-03,  4.1261e-02],\n",
-       "           [-1.3790e-02, -4.9849e-03,  1.0225e-02]],\n",
-       " \n",
-       "          [[-1.0310e-02, -1.1519e-02, -4.2256e-03],\n",
-       "           [-2.8921e-02, -2.4514e-02, -2.7419e-02],\n",
-       "           [-6.8213e-03,  5.6670e-02,  5.1814e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.9890e-02,  2.7226e-02,  1.9743e-03],\n",
-       "           [ 2.1947e-02,  1.4449e-02,  1.5748e-02],\n",
-       "           [-4.4553e-03,  2.0362e-02,  1.2021e-02]],\n",
-       " \n",
-       "          [[-1.8692e-02, -2.6291e-03,  3.5630e-02],\n",
-       "           [-1.5610e-02,  3.7182e-02,  4.4715e-02],\n",
-       "           [ 4.4212e-02,  3.8407e-02,  2.9761e-02]],\n",
-       " \n",
-       "          [[ 3.6884e-02,  6.6667e-02,  4.7087e-02],\n",
-       "           [ 2.9564e-02,  2.6791e-02,  3.5105e-03],\n",
-       "           [-1.5800e-02, -2.9620e-02, -4.4563e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-2.9598e-03,  7.8690e-03,  5.2416e-03],\n",
-       "           [-2.1239e-02, -8.6174e-03, -4.5849e-02],\n",
-       "           [ 3.8620e-02, -1.4864e-02, -5.3976e-02]],\n",
-       " \n",
-       "          [[ 3.0559e-03,  6.6720e-03,  2.4758e-02],\n",
-       "           [-1.8190e-02,  2.3206e-04,  2.4456e-02],\n",
-       "           [-2.7607e-02, -2.8217e-02, -1.2153e-02]],\n",
-       " \n",
-       "          [[-5.2155e-03, -2.7846e-02, -3.0682e-02],\n",
-       "           [-2.1107e-02, -6.6787e-02, -6.3779e-02],\n",
-       "           [-2.4757e-02, -4.3398e-02, -1.8743e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.9877e-03, -6.4910e-03,  9.4758e-04],\n",
-       "           [-2.8677e-02,  7.4954e-03,  9.6202e-04],\n",
-       "           [-2.0439e-02, -1.0334e-02,  2.9480e-03]],\n",
-       " \n",
-       "          [[ 5.1407e-02,  1.2075e-02, -4.8483e-03],\n",
-       "           [ 5.8109e-02, -3.2746e-02, -1.7231e-02],\n",
-       "           [ 4.2002e-02,  2.5390e-02,  1.0335e-02]],\n",
-       " \n",
-       "          [[-2.5604e-02, -2.0845e-02, -1.0362e-02],\n",
-       "           [-1.4444e-02,  1.1455e-02,  7.5306e-03],\n",
-       "           [-2.0389e-02, -3.1935e-04,  8.4687e-03]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2413, 0.2943, 0.1652, 0.2476, 0.2765, 0.2606, 0.2630, 0.2992, 0.2489,\n",
-       "         0.2598, 0.3037, 0.2686, 0.2899, 0.2501, 0.2544, 0.2336, 0.2366, 0.2992,\n",
-       "         0.2730, 0.2567, 0.1642, 0.2228, 0.3073, 0.2913, 0.2891, 0.3368, 0.2724,\n",
-       "         0.2749, 0.2567, 0.2587, 0.2442, 0.3049, 0.2452, 0.2874, 0.1831, 0.2198,\n",
-       "         0.2374, 0.2771, 0.2893, 0.2485, 0.2764, 0.2399, 0.2825, 0.2956, 0.2985,\n",
-       "         0.2457, 0.2335, 0.2912, 0.2945, 0.2803, 0.2415, 0.2976, 0.2792, 0.2723,\n",
-       "         0.2704, 0.2693, 0.2487, 0.3368, 0.2358, 0.3039, 0.2961, 0.2818, 0.2871,\n",
-       "         0.2801, 0.1558, 0.2971, 0.2202, 0.3077, 0.3382, 0.3338, 0.2639, 0.1593,\n",
-       "         0.2959, 0.3021, 0.2438, 0.2541, 0.3081, 0.2824, 0.2596, 0.2724, 0.2732,\n",
-       "         0.2295, 0.2406, 0.2186, 0.2637, 0.2499, 0.3042, 0.3004, 0.2752, 0.2012,\n",
-       "         0.2630, 0.3114, 0.2572, 0.2923, 0.2916, 0.2704, 0.2761, 0.2743, 0.2967,\n",
-       "         0.2556, 0.2489, 0.2843, 0.2768, 0.3094, 0.2404, 0.2470, 0.2758, 0.2386,\n",
-       "         0.2793, 0.2598, 0.1461, 0.2276, 0.2524, 0.3336, 0.2695, 0.2984, 0.2731,\n",
-       "         0.2870, 0.2738, 0.2880, 0.2700, 0.2822, 0.2638, 0.2505, 0.3161, 0.2326,\n",
-       "         0.2573, 0.2183], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.1114, -0.0904,  0.2911, -0.0246, -0.0926, -0.0765, -0.1021, -0.0618,\n",
-       "         -0.0873, -0.0045, -0.2108, -0.0477, -0.0778, -0.0909, -0.0742, -0.0433,\n",
-       "         -0.0542, -0.1348, -0.0879, -0.0766,  0.1815, -0.0106, -0.0813, -0.0953,\n",
-       "         -0.0876, -0.1276, -0.1286, -0.1369, -0.0417, -0.0469,  0.0494, -0.0776,\n",
-       "         -0.0624, -0.0948,  0.1917, -0.0544,  0.1074, -0.1817, -0.0683,  0.0160,\n",
-       "         -0.0861, -0.0603, -0.1325, -0.0709, -0.1005, -0.0671,  0.0654, -0.1528,\n",
-       "         -0.1602, -0.0486, -0.0576, -0.1293, -0.1186, -0.1562, -0.0161, -0.1085,\n",
-       "         -0.1409, -0.1484,  0.0105, -0.0751, -0.0357, -0.0878, -0.0668, -0.0604,\n",
-       "          0.1042, -0.1207,  0.0999, -0.0596, -0.0335, -0.0657, -0.0845,  0.0941,\n",
-       "         -0.1774, -0.1612, -0.0962, -0.0813, -0.1627, -0.0412, -0.0696, -0.0511,\n",
-       "         -0.0968, -0.0585, -0.0730, -0.0534, -0.0642,  0.0278, -0.0993, -0.1162,\n",
-       "         -0.0246,  0.0185, -0.2522, -0.1431, -0.0403, -0.2850, -0.1150, -0.0758,\n",
-       "         -0.0635,  0.0155, -0.0764, -0.0804, -0.0347, -0.0719, -0.0668, -0.0373,\n",
-       "         -0.0360, -0.0272, -0.0948, -0.0574, -0.0992, -0.0472,  0.3697, -0.0522,\n",
-       "         -0.0858, -0.1571, -0.0648, -0.0534, -0.0871, -0.1106, -0.0405, -0.0928,\n",
-       "         -0.0356, -0.0506, -0.0759,  0.0069, -0.1068, -0.1086,  0.0615,  0.1299],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-1.6942e-02, -2.1423e-02,  3.6683e-02],\n",
-       "           [ 4.9522e-03, -1.2667e-02,  3.4307e-02],\n",
-       "           [-3.4769e-03, -1.0481e-02,  1.7346e-03]],\n",
-       " \n",
-       "          [[ 3.2815e-02,  2.6910e-02,  5.7732e-02],\n",
-       "           [-3.3274e-02, -3.8578e-03,  6.8649e-02],\n",
-       "           [ 1.1409e-02, -3.0136e-02,  1.7862e-02]],\n",
-       " \n",
-       "          [[-2.2175e-02, -2.5743e-02,  1.8972e-03],\n",
-       "           [ 3.1877e-02, -1.2811e-01, -2.8920e-02],\n",
-       "           [ 1.6419e-02, -6.6101e-02,  3.3015e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 9.6606e-03,  1.7143e-02,  3.0644e-02],\n",
-       "           [-1.6097e-02,  1.8295e-02,  2.1650e-02],\n",
-       "           [-1.2207e-03,  9.1519e-03, -8.8119e-03]],\n",
-       " \n",
-       "          [[ 1.9903e-02,  5.7421e-02, -6.1743e-03],\n",
-       "           [ 2.1564e-02,  4.2708e-02, -1.1534e-02],\n",
-       "           [ 1.7134e-02,  2.5199e-03, -3.5181e-03]],\n",
-       " \n",
-       "          [[-2.1376e-02,  3.8527e-03, -1.6949e-02],\n",
-       "           [-3.7126e-02, -3.4701e-02,  8.0327e-03],\n",
-       "           [ 2.6734e-03, -1.1484e-02, -9.6505e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-5.5988e-04, -5.2903e-03, -3.5445e-03],\n",
-       "           [-1.0421e-02, -4.7965e-04, -1.5814e-02],\n",
-       "           [ 3.6367e-03, -1.9940e-03, -6.6842e-03]],\n",
-       " \n",
-       "          [[ 4.6355e-03,  8.6692e-04,  1.4803e-02],\n",
-       "           [-9.5985e-03,  1.3017e-02,  7.6646e-03],\n",
-       "           [ 1.0306e-02,  1.8352e-02,  1.7621e-02]],\n",
-       " \n",
-       "          [[ 2.1712e-02,  1.4898e-02,  9.2384e-03],\n",
-       "           [ 9.9632e-03,  5.5806e-03, -4.8654e-03],\n",
-       "           [ 5.1981e-03,  1.1946e-02, -3.3752e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.4278e-02,  2.3443e-02,  2.5302e-02],\n",
-       "           [ 1.8605e-02, -3.4662e-03,  1.8438e-02],\n",
-       "           [ 1.7852e-02, -1.0268e-03,  4.5419e-03]],\n",
-       " \n",
-       "          [[-8.9580e-03,  4.4993e-03,  3.2893e-03],\n",
-       "           [ 4.4691e-03, -8.1231e-03, -1.6427e-03],\n",
-       "           [-1.9626e-02, -6.6267e-03, -1.4005e-02]],\n",
-       " \n",
-       "          [[-9.9650e-03, -2.4914e-03,  2.5238e-03],\n",
-       "           [-1.7501e-02, -2.2857e-02,  2.7616e-03],\n",
-       "           [-8.9475e-03, -4.0199e-03, -3.5663e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 3.0720e-03, -2.1464e-02,  1.9565e-02],\n",
-       "           [-1.6042e-02, -2.7445e-02, -1.8734e-02],\n",
-       "           [ 1.8422e-03,  1.0892e-02,  6.7417e-03]],\n",
-       " \n",
-       "          [[-1.9924e-03,  1.4490e-02, -4.7086e-03],\n",
-       "           [-4.9832e-03, -3.0245e-02, -6.9400e-03],\n",
-       "           [-4.0416e-02, -2.0604e-02,  9.0743e-03]],\n",
-       " \n",
-       "          [[ 2.4892e-02,  3.0617e-03,  4.5404e-02],\n",
-       "           [-1.0037e-01, -1.6869e-01, -2.6829e-03],\n",
-       "           [-5.3333e-02, -4.0676e-02,  8.2094e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.6308e-03,  9.8049e-03,  1.7412e-03],\n",
-       "           [ 2.3040e-03, -1.0479e-02, -5.3106e-02],\n",
-       "           [-2.0703e-02, -6.5164e-03,  7.8745e-03]],\n",
-       " \n",
-       "          [[ 8.4042e-05, -2.3781e-03, -1.4651e-02],\n",
-       "           [ 2.2237e-02,  3.3922e-02, -2.7617e-02],\n",
-       "           [ 9.9306e-03, -6.7276e-02,  9.0720e-03]],\n",
-       " \n",
-       "          [[-1.6083e-02, -3.6638e-02,  2.0950e-02],\n",
-       "           [-9.5424e-03,  1.2136e-02,  4.3458e-02],\n",
-       "           [ 5.0853e-04, -9.4369e-03,  4.1694e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-4.0077e-02, -8.2397e-02, -4.6135e-02],\n",
-       "           [-5.2504e-02, -5.7844e-02, -4.0559e-02],\n",
-       "           [-3.2110e-02, -3.3118e-02, -5.2616e-02]],\n",
-       " \n",
-       "          [[ 1.9654e-02,  1.8035e-02,  4.2665e-03],\n",
-       "           [ 2.1831e-02,  4.0700e-02,  3.3053e-03],\n",
-       "           [-1.8227e-02, -1.4948e-02, -2.9275e-02]],\n",
-       " \n",
-       "          [[ 7.9031e-02,  1.1140e-01,  7.2378e-02],\n",
-       "           [-1.4050e-03,  3.4513e-02,  2.2394e-02],\n",
-       "           [-4.3105e-02, -8.1686e-02, -9.5038e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.0161e-03, -3.2161e-03, -6.4830e-03],\n",
-       "           [-1.0689e-02, -9.0242e-03,  2.1989e-02],\n",
-       "           [ 1.3527e-02, -4.1691e-04, -5.4819e-03]],\n",
-       " \n",
-       "          [[ 1.9562e-02,  1.5156e-02, -5.2429e-03],\n",
-       "           [ 1.5026e-02,  1.4341e-02,  2.5593e-02],\n",
-       "           [-6.0622e-03,  3.2990e-02,  3.6518e-02]],\n",
-       " \n",
-       "          [[-8.9814e-03, -1.7443e-02, -7.0637e-03],\n",
-       "           [-2.8903e-02, -2.2116e-02, -2.2158e-02],\n",
-       "           [-7.5737e-03,  3.0914e-03,  3.6265e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.8127e-02,  4.0846e-02,  3.3279e-02],\n",
-       "           [ 1.2546e-02,  5.0820e-02,  2.9284e-02],\n",
-       "           [-1.4135e-02,  3.5279e-02,  5.5206e-02]],\n",
-       " \n",
-       "          [[ 7.6051e-03, -5.3568e-03, -2.5147e-02],\n",
-       "           [ 7.7711e-04,  1.8647e-02, -1.3971e-02],\n",
-       "           [-9.4678e-03,  2.4208e-03,  1.4643e-02]],\n",
-       " \n",
-       "          [[-2.0354e-02,  4.1184e-02, -2.9226e-03],\n",
-       "           [-2.0672e-02,  2.7887e-02, -1.3404e-02],\n",
-       "           [-2.1922e-02,  4.2912e-02, -5.6269e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.4999e-02,  5.6250e-04,  1.8160e-02],\n",
-       "           [ 1.2976e-02,  1.0638e-03, -4.7798e-03],\n",
-       "           [ 3.4700e-03, -5.5385e-03, -2.0759e-02]],\n",
-       " \n",
-       "          [[-3.0960e-02, -3.0537e-02, -3.1289e-02],\n",
-       "           [-1.9793e-02, -3.8229e-02, -3.1802e-02],\n",
-       "           [-3.3119e-02, -5.1825e-02, -5.1945e-02]],\n",
-       " \n",
-       "          [[-5.1664e-02, -3.0711e-02,  1.5138e-02],\n",
-       "           [-3.2935e-02, -2.6463e-02, -4.0260e-03],\n",
-       "           [-4.0863e-02,  8.5124e-03, -5.0394e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 4.7866e-03,  1.5061e-02,  1.9693e-03],\n",
-       "           [ 1.2154e-02,  6.0345e-02,  1.7578e-02],\n",
-       "           [ 2.2573e-02, -1.9102e-02,  2.0298e-02]],\n",
-       " \n",
-       "          [[-5.4321e-03, -1.0585e-04, -2.6908e-03],\n",
-       "           [ 2.2547e-02,  1.7512e-01,  1.1868e-01],\n",
-       "           [ 1.6971e-02,  8.2587e-02, -2.7063e-03]],\n",
-       " \n",
-       "          [[-3.6041e-02, -5.1999e-03, -1.6092e-02],\n",
-       "           [ 7.9328e-03, -6.1622e-02,  1.5545e-02],\n",
-       "           [ 7.1736e-03, -4.2283e-02,  7.3255e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.3317e-02,  3.0368e-02,  1.5165e-02],\n",
-       "           [ 2.7000e-02,  1.1074e-01,  4.3010e-02],\n",
-       "           [-5.0446e-03,  5.3170e-02,  3.8671e-02]],\n",
-       " \n",
-       "          [[-4.7347e-02, -1.6027e-02, -1.0508e-02],\n",
-       "           [ 1.4845e-02,  6.3948e-02,  1.5493e-02],\n",
-       "           [ 2.6392e-02,  4.5862e-02,  5.4653e-02]],\n",
-       " \n",
-       "          [[ 2.9637e-03, -2.6872e-02, -6.5528e-02],\n",
-       "           [ 4.2170e-03, -6.8644e-03,  7.8568e-03],\n",
-       "           [ 1.1587e-02, -8.8838e-03,  3.3919e-04]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.3754, -0.0026,  0.2014,  0.2714,  0.3347,  0.4013,  0.1533,  0.1575,\n",
-       "          0.0840,  0.3171,  0.1358,  0.1632,  0.3637,  0.1611,  0.1305,  0.2856,\n",
-       "          0.3860,  0.4388,  0.1099,  0.0050,  0.4370,  0.1823,  0.3598,  0.0837,\n",
-       "          0.2579,  0.3238,  0.3918,  0.2078,  0.1271,  0.4003,  0.4178,  0.3699,\n",
-       "          0.1838,  0.1340,  0.6143,  0.1603,  0.2465,  0.2078,  0.3437,  0.0802,\n",
-       "          0.5521,  0.2934,  0.1115,  0.2124,  0.3292,  0.1430,  0.4146,  0.2169,\n",
-       "          0.2397,  0.1599,  0.3439,  0.2469,  0.1203,  0.0148,  0.2667,  0.2494,\n",
-       "          0.2203,  0.2750,  0.1483,  0.0696,  0.0120,  0.4058,  0.2660,  0.2941,\n",
-       "          0.1367,  0.2789,  0.0745,  0.2522,  0.0627,  0.2844,  0.2634,  0.2743,\n",
-       "          0.4751,  0.3396,  0.3991,  0.4223, -0.0190,  0.1774,  0.0216,  0.2291,\n",
-       "          0.1143,  0.2591,  0.3108,  0.2899,  0.3740,  0.2758,  0.2937,  0.0566,\n",
-       "          0.2964,  0.4569,  0.1097,  0.2261,  0.3042,  0.3332,  0.2983,  0.3930,\n",
-       "          0.4258,  0.2959,  0.5352,  0.2834,  0.2927,  0.3450,  0.2465,  0.3742,\n",
-       "          0.1347,  0.3564,  0.2449,  0.4021,  0.0612,  0.2785,  0.4313,  0.0101,\n",
-       "          0.2724,  0.1483,  0.3063,  0.4176,  0.0154,  0.2825,  0.2832,  0.2740,\n",
-       "          0.2617,  0.1203,  0.3162,  0.2618,  0.1919,  0.2402,  0.3416,  0.3045],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-7.9594e-02,  1.9871e-01,  5.2790e-02,  8.2986e-02,  1.2391e-01,\n",
-       "         -1.2831e-02,  1.5877e-01,  4.0202e-02,  2.3912e-01,  8.8199e-02,\n",
-       "          1.8296e-01,  1.8851e-01,  7.2331e-02,  2.1382e-01,  1.1849e-01,\n",
-       "          8.3231e-02,  8.7218e-02,  5.1576e-02,  5.9276e-02,  2.8734e-01,\n",
-       "         -1.0618e-01,  1.3883e-03,  6.9584e-02,  3.1052e-02,  6.2608e-02,\n",
-       "          7.8061e-03,  8.7516e-02, -5.9862e-02,  1.5743e-01,  5.5752e-02,\n",
-       "          7.1076e-02,  4.0790e-02,  1.0736e-01,  2.0800e-01, -1.7001e-01,\n",
-       "         -1.5292e-02,  1.0822e-01,  7.1991e-02,  2.5727e-02,  7.8831e-02,\n",
-       "         -8.1268e-02,  1.4070e-03,  8.6071e-02,  1.5431e-01, -1.1226e-01,\n",
-       "         -1.5421e-02, -4.0221e-02,  9.6446e-02,  6.5594e-02,  6.2903e-02,\n",
-       "         -1.3418e-01,  2.8321e-02,  2.3517e-01,  2.9004e-01,  7.4273e-02,\n",
-       "          1.0059e-01,  1.2725e-02,  2.0673e-02,  1.1907e-01, -7.6154e-02,\n",
-       "          3.0413e-02,  4.6972e-02,  7.2272e-02, -6.6257e-03,  7.1061e-03,\n",
-       "         -1.7461e-02, -5.8568e-02,  4.4240e-02,  1.9252e-01,  1.3827e-04,\n",
-       "          2.8973e-02,  7.2454e-02, -1.2339e-01,  9.4754e-02,  7.2224e-03,\n",
-       "         -2.7544e-02,  1.2419e-01,  1.2700e-01,  9.2134e-02,  1.3521e-01,\n",
-       "          3.6035e-02,  7.2703e-02,  6.7161e-02,  1.0189e-01,  8.0710e-03,\n",
-       "          7.8688e-03, -7.3461e-02,  2.0406e-01,  3.4207e-02,  1.7192e-02,\n",
-       "          7.0909e-02,  1.0224e-01,  7.3142e-02,  1.7498e-02,  1.6989e-01,\n",
-       "          1.1868e-01, -1.0162e-01, -5.1533e-02, -1.0334e-01,  1.4175e-01,\n",
-       "          3.6133e-02,  9.5283e-02,  6.6691e-02, -1.7817e-02,  1.4081e-01,\n",
-       "         -1.9909e-02,  1.1734e-01, -3.5408e-02,  8.6684e-02, -2.6569e-02,\n",
-       "          3.2791e-02,  1.2005e-01,  7.7315e-02,  7.6089e-02,  5.7540e-03,\n",
-       "         -1.5415e-01,  7.4621e-02,  2.0002e-01,  1.0321e-01,  5.4740e-02,\n",
-       "          8.9361e-02,  1.1972e-01,  9.2393e-02,  1.9584e-01,  6.6385e-02,\n",
-       "          8.9421e-02, -1.8073e-03,  1.9712e-01], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-0.0143]],\n",
-       " \n",
-       "          [[-0.1063]],\n",
-       " \n",
-       "          [[ 0.0429]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0386]],\n",
-       " \n",
-       "          [[-0.0020]],\n",
-       " \n",
-       "          [[ 0.1047]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0029]],\n",
-       " \n",
-       "          [[-0.0252]],\n",
-       " \n",
-       "          [[ 0.0269]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0150]],\n",
-       " \n",
-       "          [[ 0.0156]],\n",
-       " \n",
-       "          [[ 0.0402]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0179]],\n",
-       " \n",
-       "          [[-0.0336]],\n",
-       " \n",
-       "          [[-0.0111]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.1591]],\n",
-       " \n",
-       "          [[-0.0095]],\n",
-       " \n",
-       "          [[-0.0327]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0233]],\n",
-       " \n",
-       "          [[-0.0165]],\n",
-       " \n",
-       "          [[ 0.0099]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0137]],\n",
-       " \n",
-       "          [[-0.0406]],\n",
-       " \n",
-       "          [[-0.2158]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0688]],\n",
-       " \n",
-       "          [[ 0.0026]],\n",
-       " \n",
-       "          [[-0.0171]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0003]],\n",
-       " \n",
-       "          [[ 0.0657]],\n",
-       " \n",
-       "          [[-0.0354]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0969]],\n",
-       " \n",
-       "          [[ 0.0174]],\n",
-       " \n",
-       "          [[-0.0104]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0081]],\n",
-       " \n",
-       "          [[ 0.0338]],\n",
-       " \n",
-       "          [[-0.0648]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.1789,  0.3258,  0.4038,  0.0985,  0.1995,  0.1356,  0.3150,  0.1269,\n",
-       "          0.2227,  0.1650,  0.3098,  0.4136,  0.2356,  0.3851,  0.3566,  0.2873,\n",
-       "          0.1640,  0.1445,  0.3387,  0.4080,  0.1262,  0.0847,  0.0747,  0.1434,\n",
-       "          0.2653,  0.1971,  0.1809,  0.1764,  0.2022,  0.1009,  0.0668,  0.2114,\n",
-       "          0.2803,  0.3692,  0.0916,  0.0913,  0.0433,  0.3106,  0.2976,  0.2656,\n",
-       "          0.1238,  0.3869,  0.0990,  0.0328,  0.1085,  0.4747,  0.1382,  0.2009,\n",
-       "          0.1280,  0.3019,  0.1469,  0.3689,  0.3390,  0.5221,  0.2494,  0.2894,\n",
-       "          0.2213,  0.2176,  0.3575,  0.5758,  0.5178,  0.0425,  0.1238,  0.3437,\n",
-       "          0.3320,  0.3604,  0.1821,  0.0790,  0.3186,  0.0945,  0.1763,  0.3497,\n",
-       "          0.1688,  0.1699,  0.0654,  0.1510,  0.4357,  0.2622,  0.3785,  0.1120,\n",
-       "          0.3296,  0.4234,  0.4258,  0.1326,  0.0590,  0.2357,  0.1698,  0.3655,\n",
-       "          0.2481,  0.1538,  0.1921,  0.1750,  0.0688,  0.1231,  0.0352, -0.0095,\n",
-       "          0.0195,  0.2150,  0.0315,  0.0272,  0.0348,  0.3185,  0.1793,  0.0384,\n",
-       "          0.3247,  0.4240,  0.2010,  0.1492,  0.5755,  0.3195,  0.0707,  0.1985,\n",
-       "          0.1239,  0.3023,  0.0917,  0.1395,  0.1169,  0.0062,  0.0404,  0.0827,\n",
-       "          0.2876,  0.3164,  0.1571,  0.1361,  0.3053,  0.1955,  0.1454,  0.0891],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-7.9594e-02,  1.9871e-01,  5.2790e-02,  8.2986e-02,  1.2391e-01,\n",
-       "         -1.2831e-02,  1.5877e-01,  4.0202e-02,  2.3912e-01,  8.8199e-02,\n",
-       "          1.8296e-01,  1.8851e-01,  7.2331e-02,  2.1382e-01,  1.1849e-01,\n",
-       "          8.3231e-02,  8.7218e-02,  5.1576e-02,  5.9276e-02,  2.8734e-01,\n",
-       "         -1.0618e-01,  1.3883e-03,  6.9584e-02,  3.1052e-02,  6.2608e-02,\n",
-       "          7.8061e-03,  8.7516e-02, -5.9862e-02,  1.5743e-01,  5.5752e-02,\n",
-       "          7.1076e-02,  4.0790e-02,  1.0736e-01,  2.0800e-01, -1.7001e-01,\n",
-       "         -1.5292e-02,  1.0822e-01,  7.1991e-02,  2.5727e-02,  7.8831e-02,\n",
-       "         -8.1268e-02,  1.4070e-03,  8.6071e-02,  1.5431e-01, -1.1226e-01,\n",
-       "         -1.5421e-02, -4.0221e-02,  9.6446e-02,  6.5594e-02,  6.2903e-02,\n",
-       "         -1.3418e-01,  2.8321e-02,  2.3517e-01,  2.9004e-01,  7.4273e-02,\n",
-       "          1.0059e-01,  1.2725e-02,  2.0673e-02,  1.1907e-01, -7.6154e-02,\n",
-       "          3.0413e-02,  4.6972e-02,  7.2272e-02, -6.6257e-03,  7.1061e-03,\n",
-       "         -1.7461e-02, -5.8568e-02,  4.4240e-02,  1.9252e-01,  1.3827e-04,\n",
-       "          2.8973e-02,  7.2454e-02, -1.2339e-01,  9.4754e-02,  7.2224e-03,\n",
-       "         -2.7544e-02,  1.2419e-01,  1.2700e-01,  9.2134e-02,  1.3521e-01,\n",
-       "          3.6035e-02,  7.2703e-02,  6.7161e-02,  1.0189e-01,  8.0710e-03,\n",
-       "          7.8688e-03, -7.3461e-02,  2.0406e-01,  3.4207e-02,  1.7192e-02,\n",
-       "          7.0909e-02,  1.0224e-01,  7.3142e-02,  1.7498e-02,  1.6989e-01,\n",
-       "          1.1868e-01, -1.0162e-01, -5.1533e-02, -1.0334e-01,  1.4175e-01,\n",
-       "          3.6133e-02,  9.5283e-02,  6.6691e-02, -1.7817e-02,  1.4081e-01,\n",
-       "         -1.9909e-02,  1.1734e-01, -3.5408e-02,  8.6684e-02, -2.6569e-02,\n",
-       "          3.2791e-02,  1.2005e-01,  7.7315e-02,  7.6089e-02,  5.7540e-03,\n",
-       "         -1.5415e-01,  7.4621e-02,  2.0002e-01,  1.0321e-01,  5.4740e-02,\n",
-       "          8.9361e-02,  1.1972e-01,  9.2393e-02,  1.9584e-01,  6.6385e-02,\n",
-       "          8.9421e-02, -1.8073e-03,  1.9712e-01], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-3.4774e-02, -1.9020e-02,  7.8151e-03],\n",
-       "           [-3.6765e-02, -2.4257e-02,  5.0072e-03],\n",
-       "           [-1.7222e-02, -2.1232e-02, -2.0551e-02]],\n",
-       " \n",
-       "          [[-7.2944e-02,  6.7536e-02,  1.5813e-01],\n",
-       "           [-8.7192e-02,  1.9904e-02,  1.2000e-01],\n",
-       "           [-1.0599e-01, -6.9846e-02, -1.2511e-02]],\n",
-       " \n",
-       "          [[-1.5723e-03,  8.5118e-03,  1.3141e-02],\n",
-       "           [ 7.2019e-03, -8.9906e-03,  2.3274e-02],\n",
-       "           [-5.1230e-04, -1.1918e-02,  2.0731e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-9.9633e-03,  2.9148e-03, -1.8661e-03],\n",
-       "           [-3.6670e-02, -1.7681e-02, -1.5786e-02],\n",
-       "           [-1.5363e-02, -1.7693e-02, -3.3744e-02]],\n",
-       " \n",
-       "          [[-7.8749e-03, -8.8296e-03, -3.6538e-02],\n",
-       "           [ 3.0500e-03,  8.4974e-03, -1.6637e-02],\n",
-       "           [ 7.6092e-03,  4.8665e-03, -2.3987e-02]],\n",
-       " \n",
-       "          [[ 3.1283e-03, -9.4945e-03, -1.4694e-03],\n",
-       "           [ 6.0808e-03,  1.7366e-02,  2.2940e-02],\n",
-       "           [ 3.2532e-02,  3.1368e-02,  1.0490e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.6975e-03, -8.8320e-04, -1.4363e-02],\n",
-       "           [ 1.8923e-02,  2.0294e-02,  3.8368e-02],\n",
-       "           [ 1.4550e-02,  1.8120e-02,  2.2046e-02]],\n",
-       " \n",
-       "          [[ 1.2298e-02,  3.4733e-03,  1.4896e-02],\n",
-       "           [-6.9191e-03, -2.5961e-02, -2.3794e-03],\n",
-       "           [-7.5330e-04, -1.9584e-02, -2.8622e-04]],\n",
-       " \n",
-       "          [[-1.3369e-02,  1.9054e-03,  8.6779e-03],\n",
-       "           [-3.6356e-02, -3.0227e-02,  4.1530e-02],\n",
-       "           [-4.7679e-02, -1.6184e-03,  1.4060e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.9216e-03,  1.5045e-02,  2.8922e-02],\n",
-       "           [-1.8290e-02, -1.5989e-02,  4.7615e-03],\n",
-       "           [-1.6950e-02, -3.7855e-03,  2.4546e-02]],\n",
-       " \n",
-       "          [[-9.7514e-03, -1.6179e-02, -1.8466e-02],\n",
-       "           [ 4.3378e-03,  1.1634e-02,  7.9892e-03],\n",
-       "           [-1.9626e-02, -8.1448e-04,  7.3007e-03]],\n",
-       " \n",
-       "          [[-2.2337e-02, -1.5413e-02, -5.7723e-02],\n",
-       "           [ 1.1015e-02, -5.7097e-03, -1.6108e-02],\n",
-       "           [ 1.2981e-02, -1.2599e-02, -6.3854e-04]]],\n",
-       " \n",
-       " \n",
-       "         [[[-6.5667e-03, -1.5229e-03, -1.2980e-02],\n",
-       "           [-2.7317e-03,  1.0338e-02,  1.7666e-02],\n",
-       "           [ 1.1303e-02,  4.8621e-03, -7.3820e-03]],\n",
-       " \n",
-       "          [[-2.6732e-01, -2.3308e-01, -1.8549e-01],\n",
-       "           [-9.4718e-02, -3.8420e-02,  6.7916e-03],\n",
-       "           [ 1.7409e-01,  2.2532e-01,  2.4978e-01]],\n",
-       " \n",
-       "          [[-2.6611e-02,  4.4185e-03, -1.2489e-02],\n",
-       "           [-2.6587e-02, -1.1212e-02, -2.0575e-02],\n",
-       "           [-3.7200e-03, -1.4945e-02, -2.1037e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 3.4204e-02,  3.5720e-02,  3.6634e-02],\n",
-       "           [-6.2824e-03, -7.5877e-03, -1.3688e-03],\n",
-       "           [-3.0333e-02, -1.9859e-02, -1.2988e-02]],\n",
-       " \n",
-       "          [[ 6.8218e-03,  2.5020e-02,  2.0494e-02],\n",
-       "           [ 1.8450e-02,  4.1936e-02,  3.0502e-02],\n",
-       "           [ 1.5817e-02,  2.2830e-02,  2.0581e-02]],\n",
-       " \n",
-       "          [[ 2.5784e-02,  1.0056e-02, -5.4957e-04],\n",
-       "           [ 1.3018e-02, -8.9906e-03,  2.2512e-03],\n",
-       "           [ 3.7831e-03, -7.4440e-03,  6.2312e-03]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-1.1656e-02,  4.1958e-03, -1.6696e-02],\n",
-       "           [ 2.3626e-02,  2.7361e-02, -2.7220e-02],\n",
-       "           [ 8.2969e-03, -9.1000e-03, -1.3987e-02]],\n",
-       " \n",
-       "          [[ 6.5929e-03,  3.9038e-03, -1.1077e-02],\n",
-       "           [ 7.4646e-03,  2.2308e-02,  7.2101e-03],\n",
-       "           [ 1.9682e-02,  1.0997e-02,  6.2141e-03]],\n",
-       " \n",
-       "          [[ 3.3032e-02, -1.5129e-02, -5.8888e-02],\n",
-       "           [ 9.7045e-02,  5.5707e-02, -1.1643e-01],\n",
-       "           [ 8.6414e-02,  1.8737e-02, -3.2836e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.4826e-02, -3.5024e-03, -1.8170e-02],\n",
-       "           [ 2.7272e-02, -4.3048e-03, -3.2468e-02],\n",
-       "           [ 2.3445e-02,  1.8472e-03, -1.7150e-02]],\n",
-       " \n",
-       "          [[-5.1391e-02, -3.9318e-02, -2.8713e-02],\n",
-       "           [-3.4871e-02, -1.6637e-02, -2.3029e-02],\n",
-       "           [-2.0222e-02,  4.3376e-03, -1.2511e-02]],\n",
-       " \n",
-       "          [[ 1.2937e-02,  5.0669e-03, -1.5962e-02],\n",
-       "           [-5.5051e-03,  3.0603e-02,  6.4559e-03],\n",
-       "           [-1.7932e-02, -1.5458e-02,  4.2043e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-2.4565e-02, -8.3762e-03,  1.6046e-02],\n",
-       "           [-2.4800e-03,  4.7989e-03,  2.7102e-02],\n",
-       "           [ 1.3304e-02,  8.9153e-03,  1.7260e-02]],\n",
-       " \n",
-       "          [[ 2.0123e-01,  1.7259e-01,  5.6628e-02],\n",
-       "           [ 1.4006e-01,  2.9189e-02, -1.2505e-01],\n",
-       "           [-1.3552e-02, -1.9294e-01, -1.9586e-01]],\n",
-       " \n",
-       "          [[-3.3232e-03, -7.7072e-03,  3.2913e-03],\n",
-       "           [ 7.3964e-03, -1.0973e-02,  4.6299e-03],\n",
-       "           [-3.7173e-03,  2.7202e-02,  1.9465e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-8.7511e-03, -8.6604e-03,  9.7232e-04],\n",
-       "           [-2.2304e-02, -1.3385e-02, -1.4448e-02],\n",
-       "           [ 2.0154e-02,  3.1905e-02,  3.3136e-02]],\n",
-       " \n",
-       "          [[-1.3582e-04,  1.3700e-02,  1.7520e-03],\n",
-       "           [-1.7523e-03,  1.2187e-02, -2.6225e-03],\n",
-       "           [-9.0528e-03, -9.4848e-03, -7.1728e-03]],\n",
-       " \n",
-       "          [[ 5.0673e-02,  3.6953e-02,  1.0046e-02],\n",
-       "           [ 4.2906e-02, -6.4570e-04, -1.2564e-02],\n",
-       "           [-6.1551e-03,  7.9349e-04, -1.0782e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 5.1799e-03,  3.3965e-02, -4.2082e-03],\n",
-       "           [ 1.5625e-02,  4.2318e-02, -2.0351e-03],\n",
-       "           [ 6.7792e-03,  1.5458e-02,  1.3111e-02]],\n",
-       " \n",
-       "          [[-6.9566e-03,  1.0270e-02,  2.1185e-02],\n",
-       "           [ 1.1830e-02,  4.5758e-02,  2.3905e-02],\n",
-       "           [ 1.1598e-02,  1.6125e-02,  3.1595e-02]],\n",
-       " \n",
-       "          [[ 2.3815e-02,  2.5542e-02,  2.6920e-02],\n",
-       "           [ 3.8435e-02,  1.1716e-01,  7.3276e-02],\n",
-       "           [-3.8255e-02, -4.6616e-02, -1.8000e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.1320e-02,  7.4223e-03, -1.9957e-02],\n",
-       "           [-1.3208e-02, -7.1609e-03, -1.0005e-03],\n",
-       "           [-2.8803e-02,  5.0633e-03, -3.4172e-03]],\n",
-       " \n",
-       "          [[-2.6480e-02, -2.4169e-02, -8.6936e-03],\n",
-       "           [-2.4886e-02, -1.7353e-02, -1.7832e-02],\n",
-       "           [-1.6035e-02, -3.3035e-03, -5.6646e-03]],\n",
-       " \n",
-       "          [[ 4.5469e-03, -5.0096e-03,  1.2013e-02],\n",
-       "           [ 2.1184e-02,  4.6643e-02,  3.6739e-02],\n",
-       "           [-7.8873e-04, -2.5494e-02,  2.2242e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.1516, 0.2901, 0.1392, 0.3296, 0.2149, 0.3099, 0.1891, 0.1721, 0.2428,\n",
-       "         0.2191, 0.2378, 0.2112, 0.2031, 0.3232, 0.2991, 0.2421, 0.2118, 0.3021,\n",
-       "         0.2447, 0.2256, 0.3078, 0.1887, 0.2197, 0.2700, 0.1889, 0.1569, 0.3060,\n",
-       "         0.2120, 0.1970, 0.1552, 0.2533, 0.1475, 0.2245, 0.3768, 0.2215, 0.1848,\n",
-       "         0.2414, 0.1317, 0.2772, 0.2687, 0.2415, 0.1447, 0.2436, 0.2618, 0.2157,\n",
-       "         0.2498, 0.2136, 0.2529, 0.1590, 0.2295, 0.1924, 0.2652, 0.2417, 0.2538,\n",
-       "         0.2556, 0.2387, 0.1562, 0.2531, 0.1855, 0.1875, 0.3268, 0.2655, 0.1617,\n",
-       "         0.2449, 0.3048, 0.2203, 0.2402, 0.2591, 0.2177, 0.2249, 0.2710, 0.1778,\n",
-       "         0.2118, 0.2090, 0.2741, 0.2909, 0.2364, 0.1959, 0.2519, 0.2050, 0.2504,\n",
-       "         0.1346, 0.1409, 0.2010, 0.2324, 0.1847, 0.2829, 0.2731, 0.3515, 0.2567,\n",
-       "         0.2467, 0.2556, 0.1846, 0.2595, 0.2000, 0.2360, 0.2393, 0.2317, 0.2566,\n",
-       "         0.2482, 0.2120, 0.1812, 0.2102, 0.2306, 0.1871, 0.2560, 0.2033, 0.1686,\n",
-       "         0.2579, 0.2745, 0.2497, 0.2390, 0.2725, 0.4337, 0.2549, 0.3335, 0.2479,\n",
-       "         0.2837, 0.3148, 0.2675, 0.2147, 0.3133, 0.1348, 0.2642, 0.1826, 0.2563,\n",
-       "         0.1530, 0.2743], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 5.5009e-02, -2.1867e-01,  3.3545e-02, -2.4481e-01, -3.6093e-02,\n",
-       "         -9.2246e-02, -1.1371e-01,  5.3294e-02, -1.5270e-01, -1.6883e-01,\n",
-       "          1.6720e-02, -1.2267e-01, -1.6122e-02, -2.2180e-01, -3.2445e-01,\n",
-       "         -3.6659e-02, -1.6758e-01, -2.7524e-01, -9.3154e-02, -6.7639e-02,\n",
-       "         -1.7550e-01,  6.4150e-02, -7.5052e-02, -1.6765e-01, -2.0401e-01,\n",
-       "          2.6959e-02, -3.0621e-01, -1.8387e-01, -8.1845e-02,  3.6351e-02,\n",
-       "         -1.5595e-01, -1.0834e-02, -1.5567e-01, -4.2487e-01,  7.0592e-02,\n",
-       "         -8.8527e-02, -7.5550e-02,  7.8894e-02, -1.8059e-01, -1.6106e-01,\n",
-       "         -6.2958e-02,  4.7186e-02, -9.0016e-02, -2.0199e-01, -6.0831e-02,\n",
-       "         -1.7149e-01, -1.6009e-01, -8.0011e-02, -1.0474e-04, -1.3381e-01,\n",
-       "         -2.5391e-03, -3.1698e-01, -1.6807e-01, -3.2161e-01, -2.7149e-01,\n",
-       "         -1.9018e-01,  6.1691e-02, -6.1602e-02,  1.7115e-02, -8.5631e-02,\n",
-       "         -2.1452e-01, -8.6705e-02, -3.4985e-02, -1.7758e-01, -3.4000e-01,\n",
-       "         -7.9609e-02, -2.7988e-01, -2.1454e-01, -7.7833e-02, -1.6896e-01,\n",
-       "         -9.8372e-02, -1.2214e-01,  3.1933e-03,  1.6160e-02, -2.6389e-01,\n",
-       "         -1.1197e-01, -1.3585e-01, -3.7543e-02, -1.5631e-01, -2.3823e-02,\n",
-       "         -1.7749e-01,  2.2363e-02,  7.3519e-02, -1.3870e-01, -2.1328e-01,\n",
-       "         -1.1990e-01, -2.0619e-01, -2.0043e-01, -2.5969e-01, -1.5159e-01,\n",
-       "         -1.4565e-01, -1.6133e-01, -7.2989e-02, -2.2890e-01, -2.5271e-02,\n",
-       "         -9.4290e-02, -1.2799e-01, -2.3144e-01, -2.5790e-01, -1.3908e-01,\n",
-       "          1.0709e-01,  5.5308e-02, -1.6564e-01, -9.2229e-02,  1.4757e-02,\n",
-       "         -1.3689e-01, -7.2730e-02,  1.6270e-02, -1.7964e-01, -2.4894e-01,\n",
-       "         -1.3313e-02, -1.7279e-01, -2.1430e-01, -4.6520e-01, -1.9514e-01,\n",
-       "         -3.2433e-01, -1.4126e-01, -1.5596e-01, -2.1809e-01, -2.9965e-01,\n",
-       "         -8.5400e-02, -1.6779e-01,  4.0449e-02, -1.2844e-01, -1.1500e-01,\n",
-       "         -1.2230e-01,  7.4331e-02, -1.6088e-01], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 0.0630,  0.0097,  0.0492],\n",
-       "           [ 0.0212,  0.0013,  0.0501],\n",
-       "           [ 0.0207, -0.0043,  0.0275]],\n",
-       " \n",
-       "          [[-0.0130, -0.0104,  0.0428],\n",
-       "           [ 0.0012,  0.0125,  0.0414],\n",
-       "           [-0.0132, -0.0216, -0.0008]],\n",
-       " \n",
-       "          [[ 0.0053, -0.0195, -0.0351],\n",
-       "           [ 0.0138, -0.0122, -0.0177],\n",
-       "           [ 0.0085,  0.0072,  0.0047]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0068,  0.0249,  0.0395],\n",
-       "           [ 0.0572,  0.0598,  0.0517],\n",
-       "           [ 0.0091, -0.0173, -0.0223]],\n",
-       " \n",
-       "          [[ 0.0083,  0.0074,  0.0023],\n",
-       "           [ 0.0192, -0.0071,  0.0045],\n",
-       "           [ 0.0332,  0.0100, -0.0158]],\n",
-       " \n",
-       "          [[ 0.0068, -0.0146,  0.0021],\n",
-       "           [ 0.0014, -0.0227, -0.0083],\n",
-       "           [ 0.0371,  0.0181,  0.0176]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0045,  0.0136,  0.0482],\n",
-       "           [ 0.0018,  0.0241,  0.0304],\n",
-       "           [-0.0097,  0.0502,  0.0363]],\n",
-       " \n",
-       "          [[ 0.0075, -0.0094, -0.0272],\n",
-       "           [ 0.0236, -0.0042, -0.0045],\n",
-       "           [ 0.0318,  0.0067,  0.0102]],\n",
-       " \n",
-       "          [[ 0.0418,  0.0689,  0.0594],\n",
-       "           [ 0.0431,  0.0773,  0.0308],\n",
-       "           [-0.0563, -0.0278, -0.0655]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0183, -0.0370, -0.0234],\n",
-       "           [-0.0060, -0.0101, -0.0307],\n",
-       "           [-0.0210, -0.0064, -0.0321]],\n",
-       " \n",
-       "          [[ 0.0690, -0.0166, -0.0136],\n",
-       "           [-0.0149, -0.1320, -0.0478],\n",
-       "           [-0.0062, -0.0591, -0.0012]],\n",
-       " \n",
-       "          [[ 0.0027,  0.0032,  0.0050],\n",
-       "           [-0.0081, -0.0070, -0.0077],\n",
-       "           [ 0.0149,  0.0268,  0.0143]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0113,  0.0253, -0.0133],\n",
-       "           [-0.0236,  0.0134,  0.0050],\n",
-       "           [ 0.0034,  0.0426,  0.0130]],\n",
-       " \n",
-       "          [[-0.0097,  0.0502,  0.0379],\n",
-       "           [-0.0018,  0.0677,  0.0428],\n",
-       "           [ 0.0180,  0.0375,  0.0121]],\n",
-       " \n",
-       "          [[ 0.0082,  0.0020,  0.0285],\n",
-       "           [-0.0249, -0.0369,  0.0058],\n",
-       "           [-0.0144, -0.0151, -0.0033]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0046, -0.0188,  0.0168],\n",
-       "           [-0.0816, -0.0101,  0.0418],\n",
-       "           [-0.0228, -0.0287, -0.0186]],\n",
-       " \n",
-       "          [[-0.0026,  0.0034,  0.0230],\n",
-       "           [-0.0048, -0.0073,  0.0389],\n",
-       "           [ 0.0222,  0.0119,  0.0261]],\n",
-       " \n",
-       "          [[ 0.0266,  0.0333,  0.0036],\n",
-       "           [ 0.0464,  0.0514,  0.0092],\n",
-       "           [-0.0048, -0.0032,  0.0194]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-0.0018, -0.0063,  0.0192],\n",
-       "           [ 0.0113,  0.0105,  0.0179],\n",
-       "           [ 0.0303,  0.0061, -0.0055]],\n",
-       " \n",
-       "          [[-0.0002, -0.0044,  0.0083],\n",
-       "           [-0.0130, -0.0167, -0.0106],\n",
-       "           [-0.0134, -0.0221, -0.0238]],\n",
-       " \n",
-       "          [[ 0.0087,  0.0248,  0.0169],\n",
-       "           [-0.0104, -0.0213, -0.0385],\n",
-       "           [-0.0229, -0.0451, -0.0470]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0060,  0.0208, -0.0073],\n",
-       "           [-0.0059,  0.0304,  0.0454],\n",
-       "           [-0.0025,  0.0238,  0.0482]],\n",
-       " \n",
-       "          [[ 0.0127,  0.0242,  0.0347],\n",
-       "           [ 0.0224,  0.0353,  0.0441],\n",
-       "           [ 0.0424,  0.0433,  0.0486]],\n",
-       " \n",
-       "          [[-0.0095, -0.0036, -0.0262],\n",
-       "           [ 0.0238,  0.0364,  0.0272],\n",
-       "           [ 0.0297,  0.0388,  0.0534]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0082,  0.0226,  0.0179],\n",
-       "           [-0.0217,  0.0056,  0.0211],\n",
-       "           [-0.0031,  0.0389,  0.0171]],\n",
-       " \n",
-       "          [[ 0.0223,  0.0245, -0.0002],\n",
-       "           [ 0.0010,  0.0007, -0.0225],\n",
-       "           [-0.0039,  0.0076, -0.0147]],\n",
-       " \n",
-       "          [[-0.0617, -0.0037, -0.0417],\n",
-       "           [-0.0804, -0.0952, -0.0788],\n",
-       "           [-0.0187,  0.0081, -0.0149]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0685,  0.0414,  0.0712],\n",
-       "           [ 0.0265,  0.0308,  0.0478],\n",
-       "           [ 0.0041,  0.0138,  0.0582]],\n",
-       " \n",
-       "          [[ 0.0253,  0.0267,  0.0208],\n",
-       "           [-0.0022, -0.0668,  0.0150],\n",
-       "           [-0.0046,  0.0267,  0.0336]],\n",
-       " \n",
-       "          [[ 0.0281,  0.0118,  0.0117],\n",
-       "           [-0.0301, -0.0149, -0.0053],\n",
-       "           [-0.0259, -0.0174, -0.0328]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0113, -0.0006, -0.0003],\n",
-       "           [-0.0208, -0.0141, -0.0143],\n",
-       "           [-0.0288, -0.0363, -0.0273]],\n",
-       " \n",
-       "          [[-0.0054, -0.0152, -0.0070],\n",
-       "           [-0.0005, -0.0096, -0.0008],\n",
-       "           [ 0.0307,  0.0293,  0.0277]],\n",
-       " \n",
-       "          [[ 0.0090, -0.0089, -0.0146],\n",
-       "           [ 0.0108,  0.0022, -0.0007],\n",
-       "           [ 0.0181,  0.0203,  0.0020]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0172, -0.0286, -0.0114],\n",
-       "           [-0.0275, -0.0231, -0.0068],\n",
-       "           [-0.0138, -0.0272, -0.0342]],\n",
-       " \n",
-       "          [[ 0.0041,  0.0157,  0.0018],\n",
-       "           [-0.0326, -0.0440, -0.0302],\n",
-       "           [-0.0493, -0.0395, -0.0142]],\n",
-       " \n",
-       "          [[ 0.0185,  0.0217,  0.0138],\n",
-       "           [-0.0032, -0.0108,  0.0099],\n",
-       "           [-0.0161, -0.0017,  0.0121]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.1074, 0.2536, 0.1690, 0.1685, 0.0979, 0.1475, 0.0823, 0.2327, 0.1373,\n",
-       "         0.2191, 0.0484, 0.1471, 0.1143, 0.2063, 0.0834, 0.1823, 0.1364, 0.1689,\n",
-       "         0.1679, 0.2778, 0.1608, 0.2847, 0.0985, 0.4488, 0.1915, 0.1646, 0.1078,\n",
-       "         0.1825, 0.2542, 0.2185, 0.0763, 0.1295, 0.1659, 0.2492, 0.1991, 0.4800,\n",
-       "         0.1910, 0.2516, 0.1634, 0.3481, 0.1291, 0.1306, 0.2253, 0.1520, 0.3131,\n",
-       "         0.3343, 0.1122, 0.2397, 0.1945, 0.2191, 0.1855, 0.1540, 0.1679, 0.3289,\n",
-       "         0.0711, 0.2238, 0.1707, 0.1266, 0.2726, 0.3013, 0.2783, 0.0890, 0.1634,\n",
-       "         0.1002, 0.1258, 0.1709, 0.3745, 0.2216, 0.1876, 0.1838, 0.0536, 0.2080,\n",
-       "         0.1538, 0.0696, 0.0575, 0.2186, 0.3276, 0.2533, 0.3426, 0.2005, 0.2160,\n",
-       "         0.1058, 0.1145, 0.2379, 0.1375, 0.2467, 0.2832, 0.2968, 0.0969, 0.2185,\n",
-       "         0.4642, 0.1578, 0.1175, 0.1777, 0.0979, 0.0192, 0.1305, 0.0991, 0.2107,\n",
-       "         0.1305, 0.1222, 0.2400, 0.1916, 0.0900, 0.1110, 0.2035, 0.1341, 0.1959,\n",
-       "         0.2959, 0.1337, 0.1190, 0.4517, 0.1815, 0.1171, 0.2140, 0.1642, 0.3361,\n",
-       "         0.0568, 0.2492, 0.0989, 0.1008, 0.1618, 0.0930, 0.1236, 0.1279, 0.1783,\n",
-       "         0.3348, 0.0550], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-8.0032e-02, -4.9605e-01, -1.3400e-01, -2.2833e-02, -2.0502e-02,\n",
-       "         -7.4779e-02, -1.2107e-02, -6.4658e-02,  3.6576e-02, -4.8886e-02,\n",
-       "         -8.6910e-03, -1.0642e-01, -5.8520e-02, -2.5568e-01, -1.4163e-01,\n",
-       "         -1.4672e-01, -1.5994e-01, -1.0762e-01, -1.5223e-01, -5.7807e-01,\n",
-       "         -1.2140e-01,  6.8464e-02, -3.0063e-02,  4.7797e-02,  8.4376e-02,\n",
-       "         -1.2639e-01, -6.2256e-02, -1.3071e-01,  1.7080e-01, -1.3138e-01,\n",
-       "          2.8568e-02, -9.4925e-02,  3.1430e-02, -3.0474e-01, -1.3330e-01,\n",
-       "          1.5357e-01,  2.9898e-02, -1.9994e-01, -1.2532e-01, -5.4112e-02,\n",
-       "          7.6240e-02, -1.1610e-02,  1.0715e-01,  8.0011e-02,  2.1863e-01,\n",
-       "         -1.9496e-01,  5.9358e-02, -5.7334e-02, -1.0718e-01, -1.3734e-01,\n",
-       "         -6.8839e-02, -5.1001e-02, -2.2828e-01, -2.9302e-01, -6.8241e-02,\n",
-       "         -2.3564e-02,  7.5486e-02,  1.5919e-01, -2.8585e-01, -8.0806e-02,\n",
-       "         -3.6337e-01,  7.8946e-02,  1.2669e-01, -9.4639e-02, -6.3203e-02,\n",
-       "         -4.3139e-04,  7.3225e-02,  1.3516e-01, -1.2323e-01,  1.0426e-01,\n",
-       "         -5.4944e-02, -1.4186e-03,  1.3379e-01, -2.3749e-02,  1.2140e-01,\n",
-       "         -1.4531e-01, -1.4684e-01,  5.9396e-03, -1.6739e-01,  7.8589e-02,\n",
-       "         -1.4515e-01,  9.8890e-03, -6.5179e-02,  2.6651e-03, -1.3417e-02,\n",
-       "         -1.1903e-01, -9.9661e-02, -1.9784e-01, -1.3320e-01, -1.1031e-01,\n",
-       "          4.7411e-02,  1.3577e-03, -2.1344e-02, -1.7475e-01,  1.9275e-02,\n",
-       "          3.6891e-02, -8.5141e-02, -1.2788e-01, -1.5909e-01,  4.3487e-02,\n",
-       "         -4.9628e-02,  3.4186e-02,  5.4511e-02,  1.5487e-01, -1.4892e-01,\n",
-       "         -1.7968e-01, -1.1334e-01, -1.8060e-01, -2.9484e-01, -1.1211e-01,\n",
-       "         -8.5819e-02, -1.8321e-02,  2.8747e-02, -9.9275e-02, -1.2072e-01,\n",
-       "         -1.3660e-01,  7.9455e-02,  3.8276e-03,  6.8487e-02,  1.0726e-01,\n",
-       "         -1.7688e-02, -1.8298e-01, -5.8152e-02,  3.1746e-02,  2.4529e-02,\n",
-       "         -5.1207e-03,  1.5367e-01, -4.3597e-02], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 0.0387, -0.0099, -0.0038],\n",
-       "           [-0.0015,  0.0004, -0.0099],\n",
-       "           [-0.0339, -0.0227,  0.0019]],\n",
-       " \n",
-       "          [[ 0.0217, -0.0075,  0.0121],\n",
-       "           [ 0.0259,  0.0244,  0.0337],\n",
-       "           [ 0.0188,  0.0265,  0.0145]],\n",
-       " \n",
-       "          [[ 0.0341, -0.0060, -0.0235],\n",
-       "           [-0.0512, -0.0716,  0.0774],\n",
-       "           [-0.0927, -0.1139,  0.0317]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0096, -0.0278, -0.0010],\n",
-       "           [-0.0416,  0.0065, -0.0112],\n",
-       "           [-0.0383, -0.0229, -0.0246]],\n",
-       " \n",
-       "          [[ 0.0007, -0.0104, -0.0322],\n",
-       "           [-0.0392, -0.0118, -0.0219],\n",
-       "           [-0.0465, -0.0228, -0.0370]],\n",
-       " \n",
-       "          [[-0.0024, -0.0009,  0.0021],\n",
-       "           [-0.0486,  0.0263,  0.0639],\n",
-       "           [-0.0232,  0.0530, -0.0108]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0194, -0.0083, -0.0043],\n",
-       "           [ 0.0201, -0.0063, -0.0419],\n",
-       "           [-0.0020, -0.0524, -0.0374]],\n",
-       " \n",
-       "          [[ 0.0315,  0.0137,  0.0027],\n",
-       "           [-0.0006, -0.0198, -0.0201],\n",
-       "           [-0.0206, -0.0255, -0.0170]],\n",
-       " \n",
-       "          [[ 0.0455,  0.0195, -0.0510],\n",
-       "           [-0.0082, -0.0267,  0.0008],\n",
-       "           [-0.0296,  0.0117,  0.0533]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0073,  0.0451, -0.0129],\n",
-       "           [ 0.0174,  0.0614, -0.0136],\n",
-       "           [-0.0264, -0.0607, -0.0213]],\n",
-       " \n",
-       "          [[-0.0281, -0.0564, -0.0255],\n",
-       "           [-0.0074, -0.0270, -0.0041],\n",
-       "           [-0.0466, -0.0555, -0.0373]],\n",
-       " \n",
-       "          [[ 0.0237, -0.0320,  0.0056],\n",
-       "           [-0.0075, -0.0684,  0.0089],\n",
-       "           [ 0.0046, -0.0209,  0.0092]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0097,  0.0021, -0.0018],\n",
-       "           [ 0.0153,  0.0334,  0.0126],\n",
-       "           [-0.0129,  0.0227,  0.0118]],\n",
-       " \n",
-       "          [[ 0.0236, -0.0311, -0.0327],\n",
-       "           [-0.0299,  0.0138, -0.0113],\n",
-       "           [ 0.0092, -0.0366, -0.0541]],\n",
-       " \n",
-       "          [[-0.0183, -0.0128, -0.0189],\n",
-       "           [-0.0214,  0.0444, -0.0221],\n",
-       "           [-0.0055, -0.0164,  0.0002]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0157,  0.0345,  0.0136],\n",
-       "           [ 0.0212, -0.0508, -0.0254],\n",
-       "           [-0.0233, -0.0054,  0.0091]],\n",
-       " \n",
-       "          [[ 0.0027,  0.0038,  0.0070],\n",
-       "           [-0.0018, -0.0224, -0.0034],\n",
-       "           [-0.0136, -0.0032,  0.0409]],\n",
-       " \n",
-       "          [[-0.0201,  0.0007,  0.0110],\n",
-       "           [ 0.0062,  0.0231, -0.0099],\n",
-       "           [ 0.0053, -0.0062,  0.0125]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-0.0330, -0.0126,  0.0263],\n",
-       "           [-0.0235,  0.0076,  0.0263],\n",
-       "           [-0.0205, -0.0140,  0.0085]],\n",
-       " \n",
-       "          [[-0.0211, -0.0047, -0.0276],\n",
-       "           [-0.0163, -0.0008, -0.0403],\n",
-       "           [ 0.0097, -0.0130, -0.0100]],\n",
-       " \n",
-       "          [[-0.0285, -0.0064, -0.0010],\n",
-       "           [-0.0628,  0.0151,  0.0171],\n",
-       "           [-0.0191, -0.0213,  0.0138]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0515,  0.0268,  0.0064],\n",
-       "           [ 0.0329, -0.0031, -0.0285],\n",
-       "           [ 0.0435,  0.0128,  0.0042]],\n",
-       " \n",
-       "          [[-0.0213, -0.0022, -0.0081],\n",
-       "           [ 0.0111,  0.0219,  0.0159],\n",
-       "           [-0.0079,  0.0097,  0.0051]],\n",
-       " \n",
-       "          [[-0.0405, -0.0097, -0.0042],\n",
-       "           [-0.0502,  0.0414,  0.0213],\n",
-       "           [-0.0052, -0.0425, -0.0275]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0216, -0.0168, -0.0098],\n",
-       "           [-0.0462, -0.0237, -0.0260],\n",
-       "           [-0.0317, -0.0297, -0.0164]],\n",
-       " \n",
-       "          [[-0.0246, -0.0240,  0.0404],\n",
-       "           [-0.0276, -0.0337,  0.0806],\n",
-       "           [-0.0531, -0.0474, -0.0172]],\n",
-       " \n",
-       "          [[-0.0113, -0.0080, -0.0156],\n",
-       "           [-0.0364, -0.0158, -0.0198],\n",
-       "           [-0.0150, -0.0180,  0.0007]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0190, -0.0097, -0.0216],\n",
-       "           [-0.0259,  0.0124, -0.0225],\n",
-       "           [-0.0202, -0.0119,  0.0109]],\n",
-       " \n",
-       "          [[-0.0065, -0.0086,  0.0034],\n",
-       "           [ 0.0050,  0.0028,  0.0128],\n",
-       "           [ 0.0103, -0.0079,  0.0121]],\n",
-       " \n",
-       "          [[ 0.0200,  0.0311,  0.0416],\n",
-       "           [ 0.0215,  0.0036,  0.0305],\n",
-       "           [ 0.0294,  0.0224,  0.0264]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0243, -0.0059, -0.0067],\n",
-       "           [ 0.0727,  0.0109,  0.0305],\n",
-       "           [ 0.0345,  0.0065,  0.0495]],\n",
-       " \n",
-       "          [[-0.0085, -0.0166, -0.0106],\n",
-       "           [-0.0190,  0.0015, -0.0107],\n",
-       "           [-0.0067,  0.0227,  0.0064]],\n",
-       " \n",
-       "          [[-0.0426, -0.0347, -0.0418],\n",
-       "           [-0.0164, -0.0642, -0.0349],\n",
-       "           [-0.0204,  0.0123, -0.0366]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0029,  0.0149,  0.0229],\n",
-       "           [ 0.0064,  0.0496,  0.0068],\n",
-       "           [ 0.0150,  0.0215, -0.0048]],\n",
-       " \n",
-       "          [[ 0.0308,  0.0447,  0.0341],\n",
-       "           [-0.0305, -0.0078,  0.0495],\n",
-       "           [-0.0014, -0.0083,  0.0397]],\n",
-       " \n",
-       "          [[-0.0141, -0.0159, -0.0267],\n",
-       "           [-0.0015,  0.0055, -0.0484],\n",
-       "           [ 0.0051,  0.0103,  0.0194]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2843, 0.2916, 0.2687, 0.2095, 0.2254, 0.2590, 0.2255, 0.2876, 0.2640,\n",
-       "         0.2801, 0.2404, 0.2823, 0.2893, 0.3297, 0.2643, 0.2876, 0.2860, 0.2286,\n",
-       "         0.2430, 0.2340, 0.2051, 0.2885, 0.2112, 0.2899, 0.2819, 0.2316, 0.2985,\n",
-       "         0.2893, 0.2421, 0.2129, 0.2318, 0.2583, 0.3090, 0.1870, 0.2441, 0.3039,\n",
-       "         0.2406, 0.2512, 0.3105, 0.2577, 0.2230, 0.2550, 0.2549, 0.2912, 0.2323,\n",
-       "         0.2843, 0.2417, 0.2333, 0.2511, 0.2365, 0.2598, 0.2305, 0.2170, 0.2320,\n",
-       "         0.2311, 0.2755, 0.1923, 0.2382, 0.1942, 0.2466, 0.2474, 0.2123, 0.2440,\n",
-       "         0.2462, 0.2354, 0.2617, 0.2345, 0.2740, 0.2007, 0.2888, 0.2226, 0.2477,\n",
-       "         0.3194, 0.2586, 0.2064, 0.2522, 0.3114, 0.2823, 0.3044, 0.1955, 0.2771,\n",
-       "         0.2817, 0.2833, 0.2484, 0.2603, 0.2778, 0.2163, 0.2950, 0.2610, 0.2384,\n",
-       "         0.2562, 0.2189, 0.2459, 0.2488, 0.2264, 0.2206, 0.2634, 0.3943, 0.2043,\n",
-       "         0.2458, 0.2615, 0.2476, 0.2483, 0.2936, 0.2784, 0.2701, 0.2223, 0.2274,\n",
-       "         0.2215, 0.2347, 0.2162, 0.1920, 0.2606, 0.2182, 0.2578, 0.2840, 0.2748,\n",
-       "         0.2024, 0.2514, 0.2424, 0.2159, 0.2450, 0.2523, 0.2644, 0.3208, 0.2482,\n",
-       "         0.2559, 0.2596], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.2151, -0.5437, -0.1118, -0.1519, -0.0937, -0.1453, -0.1242, -0.3076,\n",
-       "         -0.1499, -0.2541, -0.2297, -0.1940, -0.2052, -0.2890, -0.1755, -0.2800,\n",
-       "         -0.2978, -0.2232, -0.1760, -0.2220, -0.1531, -0.2439, -0.2657, -0.2116,\n",
-       "         -0.1996, -0.2230, -0.2973, -0.2689, -0.2593, -0.1077, -0.1429, -0.0873,\n",
-       "         -0.1863, -0.1309, -0.2134, -0.2344, -0.1477, -0.2300, -0.2435, -0.1717,\n",
-       "         -0.1478, -0.1299, -0.1773, -0.2406, -0.1819, -0.2908, -0.2273, -0.1662,\n",
-       "         -0.1267, -0.1231, -0.1963, -0.1051, -0.2058, -0.1828, -0.1138, -0.2228,\n",
-       "         -0.1055, -0.1469, -0.1792, -0.2241, -0.1932, -0.2321, -0.2067, -0.2334,\n",
-       "         -0.1842, -0.1409, -0.2668, -0.2064, -0.1219, -0.1847, -0.1171, -0.2014,\n",
-       "         -0.1752, -0.1721, -0.0496, -0.1692, -0.4409, -0.1554, -0.1774, -0.1052,\n",
-       "         -0.1972, -0.2020, -0.3139, -0.1407, -0.2897, -0.1706, -0.1441, -0.2177,\n",
-       "         -0.2407, -0.1743, -0.3298, -0.1107, -0.1050, -0.2793, -0.1594, -0.1643,\n",
-       "         -0.1428, -0.3039, -0.0504, -0.2102, -0.1712, -0.1799, -0.1364, -0.2892,\n",
-       "         -0.3000, -0.2224, -0.1699, -0.0669, -0.1383, -0.1043, -0.1904, -0.1524,\n",
-       "         -0.2949, -0.1703, -0.3380, -0.1406, -0.2600, -0.1611, -0.3161, -0.1235,\n",
-       "         -0.1392, -0.2247, -0.1541, -0.1665, -0.2177, -0.1954, -0.1233, -0.2361],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-0.0445, -0.0466, -0.0111],\n",
-       "           [-0.0094,  0.0125, -0.0059],\n",
-       "           [ 0.0198, -0.0025,  0.0008]],\n",
-       " \n",
-       "          [[-0.0181, -0.0073, -0.0003],\n",
-       "           [ 0.0039,  0.0183, -0.0013],\n",
-       "           [-0.0172,  0.0103, -0.0305]],\n",
-       " \n",
-       "          [[-0.0100,  0.0113,  0.0482],\n",
-       "           [ 0.0067,  0.0584,  0.0349],\n",
-       "           [-0.0045,  0.0037,  0.0121]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0160, -0.0120,  0.0017],\n",
-       "           [-0.0259, -0.0083, -0.0098],\n",
-       "           [-0.0486, -0.0095, -0.0110]],\n",
-       " \n",
-       "          [[ 0.0030, -0.0072,  0.0120],\n",
-       "           [ 0.0192, -0.0050,  0.0131],\n",
-       "           [ 0.0093,  0.0244,  0.0049]],\n",
-       " \n",
-       "          [[-0.0207, -0.0435,  0.0404],\n",
-       "           [-0.0159, -0.0276,  0.0343],\n",
-       "           [-0.0012, -0.0310, -0.0077]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0363, -0.0260, -0.0295],\n",
-       "           [-0.0117, -0.0117, -0.0162],\n",
-       "           [-0.0201, -0.0176, -0.0218]],\n",
-       " \n",
-       "          [[-0.0022,  0.0048, -0.0129],\n",
-       "           [ 0.0061, -0.0122, -0.0062],\n",
-       "           [ 0.0032, -0.0008,  0.0089]],\n",
-       " \n",
-       "          [[-0.0311, -0.0220, -0.0022],\n",
-       "           [ 0.0015, -0.0046,  0.0132],\n",
-       "           [ 0.0223,  0.0281, -0.0231]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0241, -0.0253,  0.0055],\n",
-       "           [-0.0299,  0.0101,  0.0200],\n",
-       "           [ 0.0017,  0.0320,  0.0191]],\n",
-       " \n",
-       "          [[ 0.0024,  0.0780,  0.0528],\n",
-       "           [ 0.0107,  0.0739,  0.0320],\n",
-       "           [ 0.0082,  0.0480,  0.0193]],\n",
-       " \n",
-       "          [[-0.0135, -0.0031, -0.0012],\n",
-       "           [ 0.0057,  0.0150,  0.0169],\n",
-       "           [-0.0018, -0.0104, -0.0172]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0069, -0.0043, -0.0292],\n",
-       "           [ 0.0457, -0.0074, -0.0309],\n",
-       "           [-0.0122, -0.0073,  0.0247]],\n",
-       " \n",
-       "          [[ 0.0280, -0.0168,  0.0171],\n",
-       "           [-0.0058, -0.0204,  0.0151],\n",
-       "           [-0.0017,  0.0320,  0.0014]],\n",
-       " \n",
-       "          [[-0.0017,  0.0033, -0.0044],\n",
-       "           [ 0.0229,  0.0224,  0.0186],\n",
-       "           [-0.0003, -0.0128, -0.0273]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0167, -0.0155, -0.0346],\n",
-       "           [ 0.0194, -0.0199, -0.0139],\n",
-       "           [-0.0117,  0.0029, -0.0097]],\n",
-       " \n",
-       "          [[ 0.0330,  0.0163,  0.0045],\n",
-       "           [ 0.0228, -0.0005, -0.0047],\n",
-       "           [ 0.0006,  0.0132, -0.0172]],\n",
-       " \n",
-       "          [[ 0.0060,  0.0139,  0.0200],\n",
-       "           [-0.0196, -0.0305,  0.0031],\n",
-       "           [-0.0276, -0.0127, -0.0080]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0143,  0.0136,  0.0105],\n",
-       "           [ 0.0212,  0.0574,  0.0141],\n",
-       "           [ 0.0221,  0.0326,  0.0121]],\n",
-       " \n",
-       "          [[-0.0403, -0.0656, -0.0290],\n",
-       "           [-0.0006,  0.0623,  0.0728],\n",
-       "           [ 0.0322,  0.0958,  0.0701]],\n",
-       " \n",
-       "          [[ 0.0129,  0.0107, -0.0163],\n",
-       "           [ 0.0227,  0.0113,  0.0048],\n",
-       "           [-0.0060,  0.0121,  0.0015]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0055,  0.0100,  0.0349],\n",
-       "           [-0.0410, -0.0246,  0.0334],\n",
-       "           [-0.0098, -0.0217,  0.0382]],\n",
-       " \n",
-       "          [[-0.0106,  0.0063, -0.0141],\n",
-       "           [-0.0023,  0.0302,  0.0048],\n",
-       "           [-0.0106,  0.0114, -0.0041]],\n",
-       " \n",
-       "          [[-0.0117,  0.0157, -0.0179],\n",
-       "           [-0.0202, -0.0124, -0.0057],\n",
-       "           [ 0.0079,  0.0018, -0.0086]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0246,  0.0017, -0.0439],\n",
-       "           [-0.0071,  0.0213,  0.0165],\n",
-       "           [-0.0050,  0.0173,  0.0080]],\n",
-       " \n",
-       "          [[-0.0115, -0.0369, -0.0244],\n",
-       "           [-0.0051,  0.0231,  0.0053],\n",
-       "           [-0.0296, -0.0229, -0.0368]],\n",
-       " \n",
-       "          [[ 0.0049, -0.0232, -0.0493],\n",
-       "           [-0.0002, -0.0362, -0.0550],\n",
-       "           [ 0.0009, -0.0268, -0.0371]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0232,  0.0200, -0.0063],\n",
-       "           [ 0.0079, -0.0110,  0.0104],\n",
-       "           [ 0.0377,  0.0377,  0.0148]],\n",
-       " \n",
-       "          [[ 0.0369,  0.0430,  0.0178],\n",
-       "           [ 0.0249,  0.0429,  0.0290],\n",
-       "           [ 0.0230,  0.0472,  0.0193]],\n",
-       " \n",
-       "          [[-0.0436, -0.0398, -0.0030],\n",
-       "           [-0.0068, -0.0542, -0.0538],\n",
-       "           [ 0.0035, -0.0221, -0.0400]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0141,  0.0145, -0.0034],\n",
-       "           [ 0.0037,  0.0129,  0.0008],\n",
-       "           [ 0.0077,  0.0215, -0.0109]],\n",
-       " \n",
-       "          [[-0.0206, -0.0238,  0.0121],\n",
-       "           [ 0.0025, -0.0083,  0.0126],\n",
-       "           [-0.0102, -0.0057,  0.0166]],\n",
-       " \n",
-       "          [[ 0.0243, -0.0067,  0.0347],\n",
-       "           [ 0.0305,  0.0542,  0.0433],\n",
-       "           [ 0.0542,  0.0242,  0.0167]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0094, -0.0054, -0.0203],\n",
-       "           [ 0.0041,  0.0333, -0.0448],\n",
-       "           [ 0.0027, -0.0012, -0.0174]],\n",
-       " \n",
-       "          [[-0.0264, -0.0147, -0.0389],\n",
-       "           [-0.0382, -0.0274, -0.0281],\n",
-       "           [-0.0258,  0.0059, -0.0119]],\n",
-       " \n",
-       "          [[-0.0062,  0.0336,  0.0316],\n",
-       "           [-0.0079,  0.0258,  0.0202],\n",
-       "           [-0.0257, -0.0546, -0.0402]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2301, 0.1356, 0.1586, 0.2929, 0.1425, 0.2311, 0.0650, 0.1844, 0.0979,\n",
-       "         0.1683, 0.0448, 0.1352, 0.0763, 0.0944, 0.0676, 0.1346, 0.2103, 0.1687,\n",
-       "         0.1277, 0.2587, 0.1894, 0.1873, 0.1397, 0.1308, 0.2094, 0.1922, 0.1489,\n",
-       "         0.2323, 0.2452, 0.1329, 0.0958, 0.1499, 0.1105, 0.1077, 0.2169, 0.1741,\n",
-       "         0.2101, 0.1620, 0.1514, 0.2022, 0.2522, 0.1082, 0.1307, 0.1287, 0.3251,\n",
-       "         0.2487, 0.2491, 0.1754, 0.2634, 0.2026, 0.2627, 0.0865, 0.1197, 0.1967,\n",
-       "         0.1188, 0.1601, 0.2627, 0.2093, 0.0761, 0.1492, 0.1955, 0.1392, 0.1708,\n",
-       "         0.1172, 0.1043, 0.1705, 0.1423, 0.2741, 0.1102, 0.2620, 0.2106, 0.1712,\n",
-       "         0.1319, 0.1669, 0.1285, 0.1268, 0.2659, 0.2010, 0.1862, 0.1746, 0.2350,\n",
-       "         0.1286, 0.1141, 0.2479, 0.2338, 0.2928, 0.3031, 0.2284, 0.1767, 0.2227,\n",
-       "         0.1674, 0.1679, 0.1568, 0.1958, 0.1396, 0.1399, 0.3623, 0.2244, 0.3212,\n",
-       "         0.1254, 0.1659, 0.1639, 0.2270, 0.2693, 0.0543, 0.1806, 0.1176, 0.2512,\n",
-       "         0.1761, 0.1400, 0.1523, 0.1796, 0.1653, 0.0977, 0.4075, 0.3389, 0.1177,\n",
-       "         0.0756, 0.1870, 0.0983, 0.0558, 0.1158, 0.1710, 0.2271, 0.1606, 0.1711,\n",
-       "         0.2492, 0.0817], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.0617,  0.0029,  0.0021, -0.1533,  0.0499, -0.1223, -0.0633, -0.0268,\n",
-       "          0.0547, -0.0863,  0.0558, -0.1797, -0.0381, -0.1439, -0.0458, -0.0817,\n",
-       "         -0.0714, -0.0690, -0.0760,  0.0541, -0.1629, -0.0627,  0.0238, -0.2879,\n",
-       "         -0.0657, -0.1159, -0.0584,  0.0431, -0.1260, -0.1130, -0.0709, -0.1365,\n",
-       "         -0.0678, -0.0913, -0.0320, -0.2226, -0.0387, -0.0576, -0.0871, -0.3304,\n",
-       "         -0.0142, -0.1382,  0.0481,  0.0544,  0.0070, -0.2652,  0.0620,  0.0162,\n",
-       "         -0.0980, -0.0664, -0.0354, -0.0731, -0.1047, -0.3193,  0.0501, -0.0589,\n",
-       "         -0.0882,  0.0381,  0.0242, -0.0927, -0.1613,  0.0192,  0.0310, -0.0932,\n",
-       "         -0.0792, -0.0881, -0.1173, -0.0675, -0.0647, -0.1913,  0.0594, -0.1346,\n",
-       "          0.0729, -0.0817,  0.1282, -0.0801, -0.3269, -0.3111, -0.3691,  0.0366,\n",
-       "         -0.1367, -0.1619,  0.0408, -0.0702,  0.0300, -0.0678, -0.2296, -0.4003,\n",
-       "          0.1181, -0.1418, -0.2406,  0.0196,  0.0586, -0.0199,  0.0077,  0.1132,\n",
-       "         -0.1167,  0.0871,  0.0423, -0.0045,  0.0038, -0.1221, -0.0588,  0.0951,\n",
-       "         -0.0293, -0.1066,  0.1033, -0.1510,  0.0584,  0.0734,  0.0340, -0.2761,\n",
-       "         -0.0162, -0.0361, -0.1018, -0.1278, -0.0981,  0.0133,  0.0205,  0.0882,\n",
-       "          0.0035, -0.0438, -0.0042, -0.0935,  0.0388, -0.0254, -0.0985,  0.0187],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-2.6445e-03, -1.5495e-02,  3.3986e-02],\n",
-       "           [ 1.2336e-02, -2.6200e-02,  2.2567e-02],\n",
-       "           [-1.8186e-02,  2.7924e-02,  1.3928e-02]],\n",
-       " \n",
-       "          [[ 9.4712e-03,  1.0801e-02,  1.8497e-03],\n",
-       "           [ 7.6853e-03,  1.2373e-02, -3.9191e-03],\n",
-       "           [ 1.4998e-02,  3.3975e-05, -5.7085e-03]],\n",
-       " \n",
-       "          [[-5.3085e-03, -4.9962e-02, -1.5000e-02],\n",
-       "           [-2.5915e-02, -7.7704e-02,  4.8269e-02],\n",
-       "           [-4.3621e-02, -9.2572e-03,  9.8465e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.9284e-02,  3.6181e-05, -1.0751e-02],\n",
-       "           [-2.0866e-04,  1.2825e-03, -2.1111e-02],\n",
-       "           [ 2.0430e-02,  7.3275e-03, -4.2784e-02]],\n",
-       " \n",
-       "          [[ 1.8985e-02,  4.3534e-03, -4.5689e-02],\n",
-       "           [ 1.1713e-02,  6.2516e-03, -5.3141e-02],\n",
-       "           [ 6.9268e-03, -3.2114e-02, -7.8577e-02]],\n",
-       " \n",
-       "          [[ 7.5559e-03, -1.3055e-02, -4.6047e-02],\n",
-       "           [-1.0614e-02, -1.7322e-02, -9.9145e-03],\n",
-       "           [-1.8244e-02,  1.3070e-02, -8.5062e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 2.9288e-02,  3.2835e-03,  8.9939e-04],\n",
-       "           [ 2.2037e-02, -1.1474e-02, -1.1608e-02],\n",
-       "           [ 7.9302e-03, -2.3466e-02, -2.0162e-02]],\n",
-       " \n",
-       "          [[ 2.3495e-02,  1.3658e-02,  1.9527e-02],\n",
-       "           [ 6.0292e-03,  1.3068e-04,  8.1194e-03],\n",
-       "           [ 2.5206e-02,  7.5406e-03, -1.6165e-03]],\n",
-       " \n",
-       "          [[ 5.3731e-03, -4.8883e-02, -3.3142e-02],\n",
-       "           [ 1.3317e-03, -3.6146e-02, -2.5304e-02],\n",
-       "           [ 1.7496e-02,  8.2387e-03,  1.4849e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-3.5137e-02, -2.2050e-02, -3.1090e-02],\n",
-       "           [-1.2531e-02,  1.4862e-03, -2.5389e-02],\n",
-       "           [ 1.2573e-02,  2.2619e-02,  1.1035e-02]],\n",
-       " \n",
-       "          [[ 4.7254e-02, -2.2326e-02, -3.0261e-02],\n",
-       "           [ 9.5489e-02,  2.7391e-04, -3.9666e-02],\n",
-       "           [ 2.5481e-02, -1.0106e-02, -4.2039e-02]],\n",
-       " \n",
-       "          [[ 3.1124e-03, -2.8286e-04, -5.9105e-03],\n",
-       "           [-3.5587e-02, -1.8451e-02, -6.9566e-03],\n",
-       "           [-7.5508e-03, -9.3915e-03, -1.1390e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 6.2872e-04,  1.2409e-02, -1.2852e-04],\n",
-       "           [-1.0610e-02, -1.1536e-02, -4.3435e-03],\n",
-       "           [-3.8963e-03, -7.7230e-03, -1.3582e-02]],\n",
-       " \n",
-       "          [[ 7.8436e-04,  4.8041e-03,  2.5480e-03],\n",
-       "           [-1.3277e-02, -1.2071e-02, -1.8153e-02],\n",
-       "           [-2.1105e-03,  5.1095e-03, -1.2935e-02]],\n",
-       " \n",
-       "          [[-1.1432e-02,  1.4052e-02, -4.0335e-02],\n",
-       "           [-1.8310e-02, -2.0531e-02, -4.1831e-02],\n",
-       "           [-4.0274e-02, -5.3044e-02, -1.5605e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.9716e-02, -1.9390e-02, -3.4651e-02],\n",
-       "           [-3.0308e-02,  1.5570e-02, -2.5562e-03],\n",
-       "           [-4.2335e-02, -1.9050e-02, -3.0384e-02]],\n",
-       " \n",
-       "          [[ 3.8596e-03,  5.3912e-03,  4.9401e-03],\n",
-       "           [ 3.2356e-02,  6.9210e-03,  9.0350e-03],\n",
-       "           [ 2.1072e-02,  1.4900e-02,  1.2837e-02]],\n",
-       " \n",
-       "          [[ 3.4693e-02,  2.3002e-02,  2.5825e-02],\n",
-       "           [ 5.0035e-02, -3.4222e-03,  2.8144e-02],\n",
-       "           [ 5.2685e-02,  8.3606e-02,  5.6056e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 5.4084e-03,  2.2570e-02, -6.1605e-02],\n",
-       "           [ 1.6894e-02,  2.9437e-02, -6.9690e-02],\n",
-       "           [ 7.0020e-03,  5.1460e-02, -8.5286e-03]],\n",
-       " \n",
-       "          [[ 1.5189e-02, -4.0929e-03,  2.1275e-02],\n",
-       "           [ 1.8230e-02,  2.4439e-02,  1.6164e-02],\n",
-       "           [ 1.8063e-02,  1.8471e-02,  1.3980e-02]],\n",
-       " \n",
-       "          [[-2.1271e-02,  3.5195e-02, -3.1626e-03],\n",
-       "           [-8.6227e-02, -1.4472e-04,  5.2089e-02],\n",
-       "           [-7.2283e-02, -7.8503e-03,  3.4595e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 9.7476e-04,  3.7205e-02,  8.1577e-03],\n",
-       "           [-1.7474e-02,  3.9713e-02,  1.6447e-02],\n",
-       "           [ 1.7255e-02,  9.2579e-03, -7.3117e-03]],\n",
-       " \n",
-       "          [[ 2.8449e-03,  3.4526e-03, -2.0227e-02],\n",
-       "           [-3.1718e-02,  6.0320e-03, -1.4651e-02],\n",
-       "           [-4.1836e-02, -3.4286e-02, -3.3539e-02]],\n",
-       " \n",
-       "          [[ 1.3922e-02, -1.5999e-02, -2.1969e-02],\n",
-       "           [-8.4408e-03, -1.9586e-02, -3.5926e-02],\n",
-       "           [-5.8211e-03,  1.4137e-03, -2.0515e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-3.1713e-02, -6.1608e-03,  1.6795e-02],\n",
-       "           [-6.0935e-02,  5.2589e-02,  1.0742e-01],\n",
-       "           [-7.0463e-02, -2.4622e-02,  6.2952e-02]],\n",
-       " \n",
-       "          [[-4.4176e-04,  1.4973e-02, -2.2871e-03],\n",
-       "           [ 1.8784e-02,  1.7097e-02, -1.4125e-02],\n",
-       "           [ 1.1421e-02,  2.6356e-02,  1.1543e-03]],\n",
-       " \n",
-       "          [[ 1.6074e-03,  8.4456e-03, -2.2766e-02],\n",
-       "           [-4.8658e-02, -5.1398e-02,  7.4896e-02],\n",
-       "           [-1.6261e-02, -5.1520e-02, -3.6906e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.7257e-02, -1.1018e-02, -1.6201e-03],\n",
-       "           [ 4.2390e-02,  2.9592e-02, -7.3351e-02],\n",
-       "           [-1.0663e-02,  1.5915e-02, -1.0390e-02]],\n",
-       " \n",
-       "          [[ 2.4627e-02,  5.2271e-03, -1.4100e-03],\n",
-       "           [ 3.9040e-02,  2.8640e-02,  3.1926e-02],\n",
-       "           [ 3.4568e-03,  1.5394e-02,  5.4581e-02]],\n",
-       " \n",
-       "          [[-8.9550e-03, -7.8387e-03, -1.1153e-02],\n",
-       "           [ 4.4842e-02,  6.8902e-03, -1.2252e-01],\n",
-       "           [ 4.7488e-02,  2.6716e-02, -7.1658e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.5684e-02, -4.7361e-02, -3.2718e-02],\n",
-       "           [-9.9691e-03, -4.0975e-02,  5.3553e-03],\n",
-       "           [-7.0341e-02, -3.4495e-02,  1.9273e-02]],\n",
-       " \n",
-       "          [[ 1.0097e-02,  2.8859e-02,  3.2731e-02],\n",
-       "           [ 1.7363e-02,  2.2818e-02,  1.5299e-02],\n",
-       "           [ 2.6394e-02,  2.5160e-02,  4.4335e-02]],\n",
-       " \n",
-       "          [[ 5.1101e-02,  4.4897e-02,  6.2974e-03],\n",
-       "           [ 2.9514e-02,  2.1000e-03,  1.7121e-02],\n",
-       "           [ 3.4434e-02,  1.1460e-02,  4.2376e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.4936e-02, -4.5167e-03,  2.4002e-02],\n",
-       "           [ 2.3046e-02, -2.2087e-02, -1.6691e-02],\n",
-       "           [ 3.2977e-02,  2.6587e-02, -5.3880e-02]],\n",
-       " \n",
-       "          [[-4.6910e-02, -8.4981e-05, -6.3338e-03],\n",
-       "           [-1.6527e-03, -5.3149e-02, -1.2265e-02],\n",
-       "           [-5.6889e-02, -6.1513e-02,  7.7455e-03]],\n",
-       " \n",
-       "          [[-1.1078e-02, -3.3718e-02, -4.3194e-02],\n",
-       "           [-3.3981e-03, -3.6478e-02, -6.1938e-02],\n",
-       "           [ 4.0713e-03, -3.6113e-02, -3.2386e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2308, 0.1726, 0.2367, 0.2129, 0.2417, 0.2438, 0.2523, 0.2799, 0.2350,\n",
-       "         0.2537, 0.2819, 0.2245, 0.2399, 0.1986, 0.2235, 0.2395, 0.2483, 0.1925,\n",
-       "         0.2759, 0.2009, 0.2041, 0.2751, 0.2856, 0.2582, 0.1988, 0.2239, 0.2964,\n",
-       "         0.2894, 0.2403, 0.2049, 0.2546, 0.2465, 0.2333, 0.2206, 0.2438, 0.2761,\n",
-       "         0.2263, 0.2107, 0.2050, 0.2368, 0.2901, 0.1929, 0.2320, 0.2646, 0.2898,\n",
-       "         0.2391, 0.2553, 0.2038, 0.2537, 0.2270, 0.2315, 0.2137, 0.2140, 0.2035,\n",
-       "         0.2128, 0.2683, 0.2826, 0.2878, 0.2716, 0.2713, 0.2241, 0.2229, 0.2686,\n",
-       "         0.2800, 0.2468, 0.2934, 0.3137, 0.1898, 0.2297, 0.2328, 0.2463, 0.2450,\n",
-       "         0.2913, 0.2827, 0.2545, 0.2329, 0.2481, 0.2129, 0.2862, 0.2067, 0.2369,\n",
-       "         0.2355, 0.2508, 0.2658, 0.2107, 0.2470, 0.2488, 0.2759, 0.2692, 0.3638,\n",
-       "         0.2227, 0.2532, 0.1946, 0.2332, 0.2584, 0.2454, 0.1985, 0.1874, 0.2933,\n",
-       "         0.2693, 0.2402, 0.2553, 0.2055, 0.2691, 0.2106, 0.2703, 0.2187, 0.2586,\n",
-       "         0.2096, 0.2860, 0.2834, 0.2461, 0.2692, 0.3020, 0.2184, 0.2273, 0.2118,\n",
-       "         0.1885, 0.2008, 0.2687, 0.2161, 0.2260, 0.2324, 0.2075, 0.2929, 0.2230,\n",
-       "         0.2297, 0.3045], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.1747, -0.1511, -0.1582, -0.1821, -0.2064, -0.1707, -0.2491, -0.3186,\n",
-       "         -0.3143, -0.1728, -0.3018, -0.2143, -0.2104, -0.2176, -0.1901, -0.1703,\n",
-       "         -0.3379, -0.1692, -0.1771, -0.1488, -0.1949, -0.1867, -0.3630, -0.3067,\n",
-       "         -0.2297, -0.1981, -0.3571, -0.3106, -0.2486, -0.1509, -0.3265, -0.2158,\n",
-       "         -0.2597, -0.2203, -0.1856, -0.3506, -0.1915, -0.1677, -0.1957, -0.2929,\n",
-       "         -0.3693, -0.1862, -0.1689, -0.2853, -0.3380, -0.2363, -0.2244, -0.2337,\n",
-       "         -0.1612, -0.2651, -0.1731, -0.1369, -0.1038, -0.1996, -0.2282, -0.2766,\n",
-       "         -0.3719, -0.3392, -0.2201, -0.2773, -0.1523, -0.1784, -0.2894, -0.2757,\n",
-       "         -0.2858, -0.3612, -0.3941, -0.2391, -0.1697, -0.2618, -0.2685, -0.2651,\n",
-       "         -0.2724, -0.2685, -0.2746, -0.1860, -0.3391, -0.2199, -0.2646, -0.3826,\n",
-       "         -0.2901, -0.2255, -0.4083, -0.2327, -0.1737, -0.1740, -0.1331, -0.1813,\n",
-       "         -0.2667, -0.4070, -0.1826, -0.2276, -0.1614, -0.1741, -0.2570, -0.3104,\n",
-       "         -0.1275, -0.1181, -0.3169, -0.2444, -0.2588, -0.3813, -0.1979, -0.3163,\n",
-       "         -0.1967, -0.2918, -0.1837, -0.2418, -0.1377, -0.3514, -0.2868, -0.2392,\n",
-       "         -0.2693, -0.4020, -0.1766, -0.2685, -0.1967, -0.1624, -0.1931, -0.3224,\n",
-       "         -0.2240, -0.1798, -0.1644, -0.1634, -0.2423, -0.1399, -0.1815, -0.3621],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 2.2954e-02, -9.5367e-03, -1.7234e-02],\n",
-       "           [ 1.8679e-02, -1.6933e-02, -1.6526e-02],\n",
-       "           [-1.7006e-02, -2.0470e-02, -2.1081e-02]],\n",
-       " \n",
-       "          [[-9.9789e-04, -9.1845e-03, -2.0831e-02],\n",
-       "           [ 4.8472e-03, -1.7822e-02, -3.0834e-02],\n",
-       "           [ 8.6512e-03, -4.7431e-04, -1.2918e-02]],\n",
-       " \n",
-       "          [[ 1.8766e-02,  3.0091e-02,  1.2080e-02],\n",
-       "           [ 2.5777e-02,  3.3319e-02,  2.3135e-02],\n",
-       "           [ 4.5004e-02,  3.3519e-02,  2.3033e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.3066e-02, -1.5737e-02, -1.6777e-02],\n",
-       "           [ 5.3634e-02, -1.0834e-02,  1.4330e-02],\n",
-       "           [ 3.1830e-02,  2.3551e-02,  2.7453e-02]],\n",
-       " \n",
-       "          [[ 1.6579e-02,  1.5319e-03, -7.0945e-03],\n",
-       "           [ 1.1557e-02,  1.4256e-02, -1.7365e-02],\n",
-       "           [-8.5792e-03,  4.1928e-04, -1.8091e-02]],\n",
-       " \n",
-       "          [[-2.4905e-03, -2.3326e-02,  3.4805e-02],\n",
-       "           [-1.8041e-02, -3.2026e-02,  1.4070e-02],\n",
-       "           [-3.2612e-02,  2.9194e-03,  2.2468e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 9.4755e-04, -4.7013e-03, -2.2331e-03],\n",
-       "           [ 1.0588e-02,  2.1937e-03, -4.2119e-03],\n",
-       "           [ 1.3748e-02, -9.2917e-04, -3.4556e-03]],\n",
-       " \n",
-       "          [[-9.1504e-03, -5.6949e-03, -1.0592e-03],\n",
-       "           [-6.4607e-03, -3.9936e-03,  3.5844e-04],\n",
-       "           [-7.1804e-03, -5.9468e-03, -5.1749e-04]],\n",
-       " \n",
-       "          [[-7.3909e-03, -8.1801e-03, -4.9242e-03],\n",
-       "           [-2.2038e-02, -2.2733e-02, -2.5362e-02],\n",
-       "           [-2.0359e-02, -1.7666e-02, -2.3907e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.3636e-02, -2.5250e-02, -1.9057e-04],\n",
-       "           [-2.8041e-03, -1.9454e-02, -1.5043e-02],\n",
-       "           [-1.0385e-03, -2.2550e-02, -2.2857e-02]],\n",
-       " \n",
-       "          [[ 8.0991e-03,  6.9831e-03,  1.8534e-02],\n",
-       "           [ 8.9746e-03,  2.7868e-03,  1.1804e-02],\n",
-       "           [ 2.4072e-03, -4.6315e-04,  5.9542e-03]],\n",
-       " \n",
-       "          [[ 3.1840e-04, -1.7956e-02, -5.0662e-03],\n",
-       "           [ 2.6423e-03, -2.4766e-02, -3.3586e-03],\n",
-       "           [ 2.2773e-03, -1.8787e-02, -7.1982e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 9.3909e-03,  2.2523e-02,  1.9170e-02],\n",
-       "           [ 2.1044e-02,  4.0154e-02,  2.3233e-05],\n",
-       "           [ 2.2290e-02,  5.9121e-03, -1.9652e-02]],\n",
-       " \n",
-       "          [[ 3.7438e-02,  3.4484e-02,  6.6795e-03],\n",
-       "           [ 2.7460e-02,  2.2823e-02, -8.7809e-03],\n",
-       "           [ 1.0064e-02, -6.4516e-03,  5.5310e-03]],\n",
-       " \n",
-       "          [[ 1.6115e-02,  2.5728e-02,  2.3140e-02],\n",
-       "           [ 3.6392e-02,  4.1339e-02,  1.8108e-02],\n",
-       "           [ 3.4972e-02,  4.1493e-02,  2.9573e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 4.6706e-03, -2.3084e-02, -1.4186e-03],\n",
-       "           [ 2.9901e-02,  1.8464e-02,  1.8556e-03],\n",
-       "           [ 4.7281e-02,  4.1229e-02,  4.4780e-02]],\n",
-       " \n",
-       "          [[-2.0594e-02,  6.9431e-05,  1.7826e-03],\n",
-       "           [-2.4595e-02, -1.0303e-02,  9.2118e-03],\n",
-       "           [-1.5426e-02, -2.4948e-02, -1.2393e-02]],\n",
-       " \n",
-       "          [[-2.4750e-02, -1.5160e-04,  4.1945e-02],\n",
-       "           [-3.9754e-02, -1.9789e-02,  1.5330e-02],\n",
-       "           [-3.6165e-03,  1.3409e-02,  1.1629e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 4.9668e-03,  6.1385e-03, -1.1448e-02],\n",
-       "           [ 1.0449e-02,  3.7062e-03,  1.2973e-02],\n",
-       "           [ 1.1914e-03,  3.8822e-03, -5.3643e-03]],\n",
-       " \n",
-       "          [[ 1.0329e-02,  4.0911e-03, -5.9251e-03],\n",
-       "           [ 2.3570e-03, -1.7500e-02, -1.4155e-02],\n",
-       "           [-1.2672e-02, -2.7447e-02, -2.6891e-02]],\n",
-       " \n",
-       "          [[-8.7949e-03,  1.7534e-02,  1.0128e-02],\n",
-       "           [ 3.5939e-02,  3.1930e-02,  3.0307e-02],\n",
-       "           [ 4.5331e-02,  7.4919e-02,  5.4098e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.0581e-02,  7.2235e-03, -1.1275e-02],\n",
-       "           [ 4.7215e-03,  2.1043e-02,  1.5885e-02],\n",
-       "           [ 4.4408e-03,  1.0981e-02,  1.6879e-02]],\n",
-       " \n",
-       "          [[-3.1252e-02, -2.6864e-02, -3.1151e-04],\n",
-       "           [-2.0023e-02,  4.3067e-02,  8.8181e-03],\n",
-       "           [-4.9312e-03,  1.5324e-02, -1.8519e-02]],\n",
-       " \n",
-       "          [[ 2.3285e-02,  1.8454e-02,  3.7173e-02],\n",
-       "           [ 4.8069e-03,  1.8892e-02,  8.3262e-02],\n",
-       "           [ 2.3685e-02, -6.7974e-04,  4.1968e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.3241e-02,  2.1955e-02,  1.7876e-02],\n",
-       "           [ 3.2101e-02,  7.4111e-03,  1.7047e-02],\n",
-       "           [ 4.8619e-02,  2.4169e-03,  3.4360e-02]],\n",
-       " \n",
-       "          [[-1.5541e-02,  1.3216e-02, -1.0500e-02],\n",
-       "           [ 5.4443e-04,  2.0034e-02, -3.7555e-03],\n",
-       "           [-1.3686e-02,  3.8585e-03, -1.4682e-02]],\n",
-       " \n",
-       "          [[-1.5559e-02, -4.7192e-02, -1.6101e-03],\n",
-       "           [-3.5797e-02, -3.7231e-02, -6.7311e-03],\n",
-       "           [-3.8896e-02, -3.3186e-02, -3.4465e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 3.9753e-02,  2.4989e-02, -8.1829e-03],\n",
-       "           [ 1.2931e-02,  3.8061e-02,  9.0759e-03],\n",
-       "           [ 4.8301e-03,  2.2200e-02,  1.2466e-03]],\n",
-       " \n",
-       "          [[-7.2821e-03,  1.4287e-02,  2.3957e-02],\n",
-       "           [ 2.2850e-03,  1.2028e-02, -1.1181e-02],\n",
-       "           [-2.7340e-02, -1.8074e-02, -1.6033e-02]],\n",
-       " \n",
-       "          [[ 9.9400e-03, -1.1751e-02,  3.3975e-02],\n",
-       "           [ 1.0429e-02, -3.7293e-02,  3.5566e-02],\n",
-       "           [ 2.2716e-02, -3.8219e-02,  2.3373e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.9007e-02,  8.5519e-03,  7.5879e-04],\n",
-       "           [ 9.5083e-03,  7.2246e-05,  2.3926e-02],\n",
-       "           [ 6.4955e-03, -7.0521e-03,  2.1490e-02]],\n",
-       " \n",
-       "          [[ 9.6662e-03, -1.0967e-02, -1.1295e-02],\n",
-       "           [ 4.8222e-03, -1.3535e-02, -2.4399e-02],\n",
-       "           [ 8.0575e-03, -1.7559e-02, -1.6981e-02]],\n",
-       " \n",
-       "          [[-4.2092e-03, -2.1638e-02, -2.3874e-02],\n",
-       "           [ 6.7109e-03,  2.2577e-04, -5.3244e-03],\n",
-       "           [ 4.0078e-02,  1.4114e-02, -4.0963e-04]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 6.4598e-04, -3.8899e-03,  2.2590e-04],\n",
-       "           [ 5.3716e-04, -1.1815e-02,  2.2879e-02],\n",
-       "           [ 9.1556e-03, -1.8111e-02,  2.3824e-03]],\n",
-       " \n",
-       "          [[-1.5069e-02,  1.7868e-02,  2.4404e-04],\n",
-       "           [-1.3274e-02,  2.8777e-03,  2.1454e-02],\n",
-       "           [-1.8273e-03, -1.6594e-02, -1.1138e-02]],\n",
-       " \n",
-       "          [[-2.5869e-02,  8.0802e-04, -2.8446e-03],\n",
-       "           [-3.1618e-02,  4.2581e-03,  1.2300e-02],\n",
-       "           [-2.0619e-02, -2.2033e-02,  1.3565e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.2130, -0.0191,  0.2026,  0.2764,  0.1564,  0.3682,  0.1044,  0.2250,\n",
-       "          0.0009,  0.3148,  0.0641,  0.0375,  0.0774,  0.0745,  0.0476,  0.2384,\n",
-       "          0.0700,  0.2061,  0.1179,  0.2153,  0.2911,  0.2281,  0.2986,  0.0935,\n",
-       "          0.2325,  0.2360,  0.1450,  0.3302,  0.2095,  0.1410,  0.1177,  0.1107,\n",
-       "          0.0846,  0.1434,  0.1979,  0.0628,  0.2334,  0.1766,  0.2424,  0.2104,\n",
-       "          0.1917,  0.1375,  0.0816,  0.1470,  0.3981,  0.1241,  0.2233,  0.3211,\n",
-       "          0.2869,  0.1551,  0.3789,  0.1597,  0.1125,  0.1225,  0.2167,  0.2343,\n",
-       "          0.3059,  0.3266,  0.0759,  0.0544,  0.0369,  0.1612,  0.2517,  0.1821,\n",
-       "          0.1012,  0.1344,  0.0375,  0.2482,  0.0924,  0.4221,  0.2849,  0.2051,\n",
-       "          0.2151,  0.1553,  0.1665,  0.1707,  0.0532,  0.0893,  0.0801,  0.1687,\n",
-       "          0.2190,  0.1556,  0.1755,  0.3604,  0.2653,  0.1904,  0.5424,  0.2015,\n",
-       "          0.1882,  0.3839,  0.0532,  0.2283,  0.2007,  0.3024,  0.1637,  0.1300,\n",
-       "          0.2881,  0.2907,  0.2307,  0.1864,  0.2819,  0.2462,  0.3961,  0.2137,\n",
-       "          0.0180,  0.1959,  0.0981,  0.3022,  0.1121,  0.1624,  0.1962,  0.0859,\n",
-       "          0.1506,  0.1121,  0.3063,  0.3406,  0.0625,  0.0824,  0.2195,  0.1124,\n",
-       "          0.0890,  0.0356,  0.2201,  0.3170,  0.1344,  0.1850,  0.2533,  0.1054],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.0954,  0.0349, -0.2606, -0.1314, -0.0662, -0.1236,  0.1006,  0.2412,\n",
-       "          0.0209, -0.2687,  0.0221,  0.0470,  0.0589, -0.0945,  0.0773, -0.0032,\n",
-       "          0.0053, -0.1790, -0.0703,  0.1954, -0.1390,  0.0251, -0.1189,  0.0534,\n",
-       "         -0.2897,  0.2281, -0.0576,  0.1308, -0.2223,  0.0829, -0.0070,  0.0137,\n",
-       "          0.0042, -0.1804,  0.0775,  0.1011,  0.0666, -0.2399, -0.0129, -0.1354,\n",
-       "         -0.1429,  0.0969,  0.0448,  0.0559, -0.4990, -0.0304, -0.1999,  0.0490,\n",
-       "         -0.1481,  0.1322, -0.1306, -0.0558, -0.0128, -0.0934,  0.0985, -0.1790,\n",
-       "         -0.0167, -0.2663, -0.0267,  0.0569,  0.0282, -0.0207,  0.0143,  0.0021,\n",
-       "         -0.1004, -0.1185,  0.0141, -0.1090,  0.0814, -0.1596, -0.0524, -0.3286,\n",
-       "          0.0251, -0.1055,  0.0323, -0.0288, -0.0536, -0.1135, -0.1312,  0.0522,\n",
-       "          0.0498, -0.1308, -0.1935, -0.3383, -0.1764, -0.0773, -0.1034, -0.1432,\n",
-       "          0.0690, -0.1002, -0.0030,  0.0557,  0.0822,  0.0250, -0.0099,  0.0170,\n",
-       "         -0.1535,  0.2030,  0.0270,  0.0717, -0.0377, -0.1869, -0.3516, -0.1963,\n",
-       "          0.0240, -0.1657,  0.0630,  0.0259,  0.0808,  0.0503,  0.0800,  0.0685,\n",
-       "          0.0321, -0.0733, -0.0787, -0.2111,  0.0267,  0.0625,  0.0167,  0.0728,\n",
-       "         -0.0107,  0.0137, -0.0146, -0.4078, -0.1027, -0.1268, -0.0986, -0.1400],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 6.9064e-03, -6.1807e-03,  1.2300e-02],\n",
-       "           [-2.4530e-02, -1.2702e-02,  1.9792e-03],\n",
-       "           [-8.0877e-03, -3.0709e-02, -2.3640e-02]],\n",
-       " \n",
-       "          [[-3.7205e-03, -8.8238e-04,  7.7730e-03],\n",
-       "           [ 8.8802e-03, -5.7426e-04,  1.0290e-02],\n",
-       "           [ 4.1596e-02,  2.7421e-02,  2.9790e-02]],\n",
-       " \n",
-       "          [[ 4.1602e-03,  1.4447e-02,  5.8999e-03],\n",
-       "           [ 1.0583e-02, -1.2054e-02, -1.4539e-02],\n",
-       "           [ 1.2163e-02,  8.5917e-03,  1.0904e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 3.1577e-02,  3.5323e-02,  3.2013e-02],\n",
-       "           [ 4.1112e-02,  2.7056e-02,  2.4781e-02],\n",
-       "           [-3.1946e-03, -1.1443e-02,  6.3416e-03]],\n",
-       " \n",
-       "          [[-4.7621e-03, -1.8818e-02, -4.4648e-03],\n",
-       "           [-2.2141e-03, -9.2939e-03,  1.8826e-02],\n",
-       "           [-4.0040e-03, -3.3074e-02,  8.0416e-03]],\n",
-       " \n",
-       "          [[ 5.5107e-03,  1.8896e-02,  8.0606e-03],\n",
-       "           [-1.6217e-02,  4.1076e-03,  7.9247e-03],\n",
-       "           [-1.9666e-03,  1.3979e-02,  1.0067e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.5449e-05, -1.9195e-02, -3.0862e-02],\n",
-       "           [-1.8576e-02, -4.6153e-02, -5.6233e-02],\n",
-       "           [ 1.6267e-02, -1.0931e-02, -2.3630e-02]],\n",
-       " \n",
-       "          [[ 7.4727e-03, -4.5083e-03,  2.0353e-03],\n",
-       "           [ 2.4023e-02,  2.3893e-02, -1.6086e-02],\n",
-       "           [ 2.6634e-02,  2.5273e-02,  1.2695e-02]],\n",
-       " \n",
-       "          [[ 2.0192e-02,  9.3045e-03, -1.8675e-03],\n",
-       "           [ 2.4077e-02, -1.4831e-02, -1.6902e-02],\n",
-       "           [ 5.3644e-02,  5.3347e-02, -1.0800e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.5660e-02, -1.5211e-03, -1.1881e-02],\n",
-       "           [-7.9252e-03,  1.7721e-02, -1.5618e-02],\n",
-       "           [ 6.2609e-03,  3.3761e-02,  1.4463e-02]],\n",
-       " \n",
-       "          [[ 1.7625e-03,  7.8739e-03,  2.5805e-02],\n",
-       "           [-3.4494e-02, -1.8188e-02,  6.7018e-03],\n",
-       "           [-1.1909e-02, -4.5872e-02, -2.9310e-02]],\n",
-       " \n",
-       "          [[ 2.4145e-03,  1.7321e-02,  1.1980e-02],\n",
-       "           [ 3.7405e-02,  2.5339e-02,  1.6295e-02],\n",
-       "           [ 5.2366e-02,  4.7028e-02,  1.4636e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-5.2448e-04, -2.4623e-03,  9.7996e-03],\n",
-       "           [-1.8337e-03,  1.5366e-02,  1.6936e-02],\n",
-       "           [-4.1270e-02, -1.7346e-02, -1.2176e-02]],\n",
-       " \n",
-       "          [[ 6.4902e-03,  4.8444e-03, -8.4546e-03],\n",
-       "           [ 1.7452e-02,  3.7923e-02,  1.3391e-02],\n",
-       "           [ 9.9169e-04,  9.0618e-03, -1.5877e-02]],\n",
-       " \n",
-       "          [[-8.7896e-03, -1.6802e-02,  8.3500e-04],\n",
-       "           [-1.1344e-02, -2.8352e-02, -1.8811e-02],\n",
-       "           [-2.4492e-02, -1.2802e-02, -3.3771e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 3.0282e-02,  2.1289e-02,  1.3378e-02],\n",
-       "           [ 7.2440e-03,  1.4237e-02, -1.0317e-02],\n",
-       "           [-1.2127e-03,  5.7735e-03,  7.4342e-03]],\n",
-       " \n",
-       "          [[-1.0478e-02, -1.2441e-02,  2.0754e-02],\n",
-       "           [-1.8735e-02, -1.8361e-02,  3.0999e-02],\n",
-       "           [-1.3811e-03, -3.3441e-03,  2.9580e-02]],\n",
-       " \n",
-       "          [[-3.8269e-02, -3.1254e-02, -6.3332e-02],\n",
-       "           [-4.2144e-02, -3.8968e-02, -4.4989e-02],\n",
-       "           [-4.0127e-02, -1.6716e-02, -2.3410e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 9.2242e-04,  5.5109e-03,  2.2194e-03],\n",
-       "           [ 1.5734e-02,  8.3117e-03, -1.7227e-02],\n",
-       "           [-3.4412e-03, -1.4623e-02,  4.1933e-03]],\n",
-       " \n",
-       "          [[-9.5510e-03, -1.4439e-02, -2.8053e-04],\n",
-       "           [-6.4165e-03,  3.0527e-03, -2.3866e-03],\n",
-       "           [-1.1494e-02, -9.4120e-03, -1.9780e-03]],\n",
-       " \n",
-       "          [[-6.9569e-03,  8.4735e-03,  1.5540e-02],\n",
-       "           [-3.8014e-04, -1.2487e-02,  9.4198e-03],\n",
-       "           [-1.8404e-03, -1.0077e-02, -1.4285e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.7708e-03,  1.8130e-02,  3.3914e-02],\n",
-       "           [-3.0983e-02, -4.2214e-02, -2.6117e-03],\n",
-       "           [-2.4546e-02, -2.8495e-02, -4.4107e-02]],\n",
-       " \n",
-       "          [[-3.0927e-02, -4.7622e-02, -3.6627e-02],\n",
-       "           [-4.2397e-02, -2.0395e-03, -4.5430e-02],\n",
-       "           [-2.4291e-02, -1.1145e-02, -3.7145e-02]],\n",
-       " \n",
-       "          [[ 5.5153e-02,  9.1103e-02,  8.0436e-02],\n",
-       "           [ 8.6889e-04,  4.0061e-02,  5.5179e-02],\n",
-       "           [-3.7739e-02, -7.0844e-02, -1.1675e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 2.3318e-02, -6.4750e-03, -1.8188e-02],\n",
-       "           [-1.9230e-02, -3.4155e-03,  6.3260e-03],\n",
-       "           [ 1.3728e-02,  1.6273e-03, -1.3002e-02]],\n",
-       " \n",
-       "          [[ 2.5560e-02,  7.6357e-03,  2.2456e-02],\n",
-       "           [-1.8674e-02, -8.9179e-03, -1.3920e-02],\n",
-       "           [-4.4644e-02, -6.2199e-02, -6.6635e-02]],\n",
-       " \n",
-       "          [[ 2.5564e-02,  2.5642e-03, -2.5182e-03],\n",
-       "           [-1.7185e-03, -1.9337e-02,  1.9863e-02],\n",
-       "           [-1.5252e-02, -9.4801e-03,  1.5882e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.3795e-02,  1.9834e-02,  1.5973e-02],\n",
-       "           [-8.6642e-03,  7.4118e-03,  7.0874e-03],\n",
-       "           [-2.4154e-02, -1.5975e-02, -2.1224e-02]],\n",
-       " \n",
-       "          [[-2.4663e-03, -2.0842e-02, -2.5455e-02],\n",
-       "           [ 3.2730e-03, -7.3658e-03, -1.4060e-02],\n",
-       "           [-3.8299e-03,  2.6372e-04, -6.6285e-03]],\n",
-       " \n",
-       "          [[ 1.9860e-02,  3.0646e-02,  2.1909e-02],\n",
-       "           [ 6.6338e-04,  2.9803e-02,  3.5836e-02],\n",
-       "           [ 6.9465e-03,  9.3147e-04,  5.5300e-04]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 4.4924e-03,  8.4398e-03,  1.3462e-03],\n",
-       "           [ 2.4168e-02, -1.0483e-02,  3.4693e-03],\n",
-       "           [ 2.0033e-02,  4.8536e-03, -1.9882e-03]],\n",
-       " \n",
-       "          [[-1.6422e-02, -1.0208e-02, -1.2094e-02],\n",
-       "           [-2.2258e-02, -1.2789e-03,  7.0875e-03],\n",
-       "           [-1.2970e-02, -1.5956e-02,  5.5325e-03]],\n",
-       " \n",
-       "          [[-6.0336e-03,  3.3052e-04,  7.2803e-03],\n",
-       "           [-2.1695e-02, -3.6302e-02, -2.0626e-02],\n",
-       "           [-2.9451e-03, -3.6453e-02, -5.7328e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-4.4151e-04, -1.0533e-02, -8.8965e-03],\n",
-       "           [ 2.8594e-04,  6.5533e-03, -1.1241e-02],\n",
-       "           [-5.6208e-03, -1.9277e-02, -1.5573e-03]],\n",
-       " \n",
-       "          [[ 1.2482e-02,  3.1079e-03, -9.4034e-03],\n",
-       "           [ 3.1835e-02,  1.8598e-02,  1.3732e-02],\n",
-       "           [ 2.4996e-02,  6.5386e-02,  3.9185e-02]],\n",
-       " \n",
-       "          [[-2.3080e-02, -3.3721e-02, -6.9174e-03],\n",
-       "           [ 6.6601e-03, -1.8012e-03, -1.7380e-02],\n",
-       "           [-2.8293e-03, -1.3672e-02, -3.4157e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2769, 0.2580, 0.2585, 0.2231, 0.2398, 0.2809, 0.2270, 0.2316, 0.2556,\n",
-       "         0.2380, 0.2389, 0.2213, 0.2103, 0.2894, 0.2031, 0.2405, 0.2642, 0.3015,\n",
-       "         0.3009, 0.2689, 0.2508, 0.2574, 0.2523, 0.2842, 0.3091, 0.3411, 0.2038,\n",
-       "         0.2835, 0.2751, 0.2417, 0.2901, 0.3206, 0.2754, 0.2635, 0.2818, 0.2412,\n",
-       "         0.2493, 0.2395, 0.2478, 0.2856, 0.2836, 0.2859, 0.2992, 0.2485, 0.2699,\n",
-       "         0.2919, 0.2850, 0.3130, 0.2848, 0.2933, 0.2732, 0.3024, 0.3019, 0.3130,\n",
-       "         0.2828, 0.2702, 0.3001, 0.3135, 0.2646, 0.2962, 0.2839, 0.3102, 0.2969,\n",
-       "         0.2562, 0.2101, 0.2327, 0.2989, 0.2834, 0.2947, 0.2621, 0.2947, 0.2263,\n",
-       "         0.2670, 0.2403, 0.2645, 0.3131, 0.2646, 0.2628, 0.2561, 0.2449, 0.3482,\n",
-       "         0.2700, 0.2367, 0.2742, 0.2536, 0.2099, 0.2838, 0.3586, 0.2337, 0.2424,\n",
-       "         0.3201, 0.2906, 0.2783, 0.2887, 0.2647, 0.3002, 0.3232, 0.2849, 0.2309,\n",
-       "         0.2409, 0.2748, 0.2843, 0.2933, 0.2866, 0.2497, 0.2453, 0.2851, 0.2797,\n",
-       "         0.2722, 0.2839, 0.2908, 0.2981, 0.2538, 0.2576, 0.2780, 0.3066, 0.2874,\n",
-       "         0.1878, 0.2568, 0.2794, 0.2526, 0.2714, 0.3073, 0.2920, 0.2965, 0.2992,\n",
-       "         0.2675, 0.2715, 0.2702, 0.2783, 0.2932, 0.3760, 0.4915, 0.2031, 0.2702,\n",
-       "         0.2802, 0.3138, 0.2418, 0.2619, 0.2461, 0.2838, 0.1968, 0.2703, 0.2435,\n",
-       "         0.1807, 0.2688, 0.3211, 0.2578, 0.3112, 0.2584, 0.2907, 0.2729, 0.2437,\n",
-       "         0.2773, 0.2450, 0.3095, 0.2807, 0.2700, 0.3088, 0.2375, 0.2566, 0.3332,\n",
-       "         0.2694, 0.2964, 0.2295, 0.3257, 0.2222, 0.2836, 0.2249, 0.2967, 0.2777,\n",
-       "         0.2782, 0.3047, 0.2445, 0.2695, 0.2600, 0.2615, 0.2620, 0.2384, 0.2446,\n",
-       "         0.2945, 0.2458, 0.2643, 0.2661, 0.1974, 0.2981, 0.2222, 0.3122, 0.2354,\n",
-       "         0.2200, 0.2584, 0.3003, 0.2936, 0.2631, 0.3262, 0.2284, 0.2976, 0.2502,\n",
-       "         0.3179, 0.2784, 0.2654, 0.2558, 0.2629, 0.2615, 0.2886, 0.2934, 0.2929,\n",
-       "         0.2811, 0.2953, 0.2609, 0.2983, 0.2757, 0.3131, 0.2913, 0.2827, 0.3314,\n",
-       "         0.2700, 0.2926, 0.2093, 0.2619, 0.2604, 0.2831, 0.2202, 0.3412, 0.2736,\n",
-       "         0.3377, 0.2964, 0.2628, 0.2432, 0.2790, 0.2580, 0.2872, 0.3021, 0.2231,\n",
-       "         0.3164, 0.2827, 0.2462, 0.2628, 0.3256, 0.2674, 0.2643, 0.2891, 0.2764,\n",
-       "         0.2225, 0.2992, 0.2709, 0.2757, 0.2808, 0.3013, 0.3138, 0.3249, 0.2397,\n",
-       "         0.2829, 0.2656, 0.2925, 0.2875], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.0428, -0.0868, -0.0939,  0.0615,  0.0308, -0.1189, -0.0183, -0.0800,\n",
-       "         -0.0928, -0.0227, -0.0018, -0.0099,  0.0565, -0.0894,  0.0342, -0.0041,\n",
-       "         -0.1227, -0.1182, -0.1017, -0.0243, -0.0343, -0.1138, -0.0574, -0.0525,\n",
-       "         -0.1683, -0.1910,  0.0927, -0.0837, -0.1025, -0.0857, -0.1178, -0.0021,\n",
-       "         -0.0921, -0.0301, -0.0969, -0.0164, -0.0818, -0.0447, -0.1358, -0.0160,\n",
-       "         -0.1894, -0.0457, -0.1693, -0.0295, -0.0332, -0.1137, -0.0878, -0.1325,\n",
-       "         -0.1128, -0.2104, -0.0858, -0.0816, -0.1648, -0.1839, -0.1169, -0.0770,\n",
-       "         -0.1408, -0.1785, -0.0710, -0.0580, -0.0524, -0.2418, -0.0998, -0.0742,\n",
-       "         -0.0534, -0.0200, -0.1163, -0.1320, -0.1032,  0.0004, -0.1016,  0.0127,\n",
-       "         -0.1108, -0.0939, -0.0075, -0.1310, -0.0678, -0.0655, -0.0410, -0.0478,\n",
-       "         -0.1512, -0.1245, -0.0242, -0.1254, -0.0734,  0.1364, -0.1003, -0.3353,\n",
-       "          0.0173,  0.0894, -0.1601, -0.0615, -0.1900, -0.1544, -0.1030, -0.1246,\n",
-       "         -0.1595, -0.0662, -0.0257, -0.0358, -0.1246, -0.0099, -0.0813, -0.1331,\n",
-       "         -0.0492, -0.0080, -0.0589, -0.0875, -0.0807, -0.1483, -0.1187, -0.1376,\n",
-       "         -0.0446, -0.1273, -0.0768, -0.2263, -0.1161, -0.0070, -0.1265, -0.1091,\n",
-       "          0.0084, -0.1312, -0.1786, -0.1816, -0.1535, -0.0882, -0.1229, -0.1045,\n",
-       "         -0.0919, -0.1051, -0.1472, -0.1367, -0.3150,  0.0614, -0.1305, -0.1194,\n",
-       "         -0.1031, -0.1012, -0.0937, -0.0299, -0.1403,  0.0710, -0.1547, -0.0853,\n",
-       "          0.1235, -0.0723, -0.0781,  0.0118, -0.0750, -0.0761, -0.2049, -0.0689,\n",
-       "         -0.0214, -0.0757, -0.0518, -0.1832, -0.1302,  0.0222, -0.1754, -0.1302,\n",
-       "         -0.1629, -0.0881, -0.0653, -0.0864,  0.0200, -0.1419,  0.0308, -0.1272,\n",
-       "         -0.0557, -0.1213, -0.1342, -0.0472, -0.0870, -0.0113, -0.0861, -0.0683,\n",
-       "         -0.0971, -0.1151,  0.0268, -0.0653, -0.1343, -0.0391, -0.0235, -0.0976,\n",
-       "          0.0815, -0.1638, -0.0261, -0.1054, -0.0075, -0.0425, -0.1100, -0.1129,\n",
-       "         -0.0675, -0.0939, -0.1888,  0.0769, -0.1467, -0.0711, -0.1729, -0.0453,\n",
-       "         -0.1664, -0.0998, -0.1002, -0.0720, -0.1155, -0.0140, -0.0176, -0.1327,\n",
-       "         -0.0345, -0.0301, -0.1139, -0.1242, -0.1271, -0.1195, -0.0864, -0.1471,\n",
-       "         -0.1187, -0.0920,  0.1025, -0.1777, -0.0463, -0.0308, -0.0664, -0.1374,\n",
-       "         -0.1067, -0.1651, -0.1778, -0.0529, -0.0747, -0.0895, -0.0274, -0.1168,\n",
-       "         -0.0989,  0.0033, -0.1437, -0.1057, -0.0337, -0.1242, -0.1303, -0.0761,\n",
-       "         -0.1604, -0.0878, -0.0789, -0.0082, -0.1235, -0.0367, -0.1413, -0.0701,\n",
-       "         -0.1243, -0.0182, -0.2554,  0.0161, -0.0619, -0.1008, -0.2003, -0.1178],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 5.6701e-03,  7.1136e-03, -6.5123e-03],\n",
-       "           [-3.1611e-03, -2.1853e-03, -1.1956e-02],\n",
-       "           [ 1.5312e-02, -3.6781e-02,  3.3610e-03]],\n",
-       " \n",
-       "          [[ 2.1162e-03,  7.9808e-03, -5.9475e-03],\n",
-       "           [ 5.3457e-03,  5.7125e-02,  3.7493e-02],\n",
-       "           [ 5.2398e-03, -2.5662e-02, -9.5264e-03]],\n",
-       " \n",
-       "          [[ 1.6858e-02,  1.6018e-02,  1.4342e-02],\n",
-       "           [ 3.0971e-03, -5.0814e-03,  6.3117e-03],\n",
-       "           [-2.3059e-02, -2.1786e-02, -5.1354e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.1632e-03, -8.8757e-03,  1.7803e-03],\n",
-       "           [-1.5148e-02,  8.6933e-03, -3.7847e-02],\n",
-       "           [-2.8076e-02, -1.4394e-02, -3.7316e-02]],\n",
-       " \n",
-       "          [[-7.3953e-03,  8.6683e-03,  2.2227e-02],\n",
-       "           [-1.8735e-02, -1.8756e-02,  1.1935e-03],\n",
-       "           [ 1.5625e-03, -4.8797e-03,  2.3232e-04]],\n",
-       " \n",
-       "          [[ 2.3044e-03,  3.6398e-03,  1.3643e-02],\n",
-       "           [ 2.6425e-03, -3.6387e-02, -6.6581e-04],\n",
-       "           [-9.3540e-03, -1.9286e-02,  3.0826e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.0279e-03, -4.2866e-02, -6.2846e-03],\n",
-       "           [-3.5655e-02, -3.4051e-02, -1.6337e-02],\n",
-       "           [-2.1996e-02, -4.5927e-02, -3.0581e-02]],\n",
-       " \n",
-       "          [[-3.1837e-02,  1.7376e-02,  8.5032e-03],\n",
-       "           [-1.3636e-02,  6.9856e-03,  3.3056e-02],\n",
-       "           [-1.5833e-02, -5.3758e-02,  2.0675e-02]],\n",
-       " \n",
-       "          [[-7.3737e-03,  6.5268e-04, -7.0178e-03],\n",
-       "           [-1.2706e-02,  9.6946e-03,  3.7760e-03],\n",
-       "           [ 2.4080e-02,  9.4760e-03, -1.2455e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.9369e-02, -7.4280e-03,  2.2270e-02],\n",
-       "           [ 7.8765e-03, -2.4332e-02,  8.5516e-03],\n",
-       "           [-2.1992e-02, -3.5375e-03, -7.7390e-03]],\n",
-       " \n",
-       "          [[-2.0347e-02, -2.0024e-02, -6.6882e-03],\n",
-       "           [ 1.0203e-02,  8.5344e-03, -8.9127e-03],\n",
-       "           [-1.1732e-03,  2.3583e-02,  4.0284e-04]],\n",
-       " \n",
-       "          [[ 8.0353e-03,  8.2633e-03,  5.6466e-03],\n",
-       "           [-1.3847e-02, -1.9453e-02, -1.1462e-02],\n",
-       "           [ 1.6392e-02, -7.1141e-03, -1.4565e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.0863e-02, -6.9110e-03, -9.4717e-03],\n",
-       "           [ 2.5258e-03,  3.2279e-02, -2.8244e-03],\n",
-       "           [ 4.6593e-03,  8.3830e-03, -7.7513e-03]],\n",
-       " \n",
-       "          [[ 1.8359e-02,  2.2422e-02, -1.2785e-02],\n",
-       "           [-2.3308e-02, -4.6909e-03,  1.5828e-02],\n",
-       "           [-6.8338e-03, -4.1773e-03,  5.8247e-03]],\n",
-       " \n",
-       "          [[-1.7564e-02, -1.6285e-02, -3.0252e-02],\n",
-       "           [-1.3690e-02,  1.8081e-02, -1.9820e-02],\n",
-       "           [-3.3379e-02, -1.5844e-02, -1.4359e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.1781e-02,  1.9141e-02,  1.8708e-02],\n",
-       "           [ 2.4891e-02, -1.5615e-02,  4.2256e-04],\n",
-       "           [ 3.1026e-02,  2.4975e-02,  1.3414e-02]],\n",
-       " \n",
-       "          [[ 8.0321e-03, -2.1060e-02, -1.2140e-02],\n",
-       "           [ 1.8143e-03,  1.5761e-03, -4.4502e-03],\n",
-       "           [ 1.8651e-02,  1.4761e-02,  3.1154e-04]],\n",
-       " \n",
-       "          [[ 2.4280e-02,  2.8579e-02,  1.9646e-02],\n",
-       "           [ 1.7653e-02, -4.6237e-04, -4.9586e-03],\n",
-       "           [ 3.0328e-03, -1.5973e-02, -1.1757e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 8.1616e-03,  1.7394e-02, -6.5632e-03],\n",
-       "           [ 1.0002e-03,  1.1142e-04,  5.4772e-03],\n",
-       "           [-1.5986e-02, -1.9179e-02,  9.1679e-04]],\n",
-       " \n",
-       "          [[ 9.7801e-05,  2.1175e-03,  6.3324e-03],\n",
-       "           [-2.2321e-02,  5.3291e-03, -1.6014e-02],\n",
-       "           [ 1.0223e-02, -1.1121e-04, -7.2569e-03]],\n",
-       " \n",
-       "          [[ 7.4379e-03,  3.0588e-02,  1.2920e-02],\n",
-       "           [-1.3750e-02, -6.5515e-03,  1.7404e-02],\n",
-       "           [-4.3856e-03, -7.5082e-04,  1.1270e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 4.2595e-03,  3.4119e-02,  1.9571e-03],\n",
-       "           [ 1.3854e-02,  1.7043e-02,  1.6153e-02],\n",
-       "           [ 1.0275e-02, -6.1247e-03, -1.9090e-03]],\n",
-       " \n",
-       "          [[-4.0316e-03, -5.8955e-03, -1.2408e-02],\n",
-       "           [ 8.1241e-03,  7.9888e-02, -8.6994e-03],\n",
-       "           [ 4.6913e-03,  6.7049e-02,  7.7171e-03]],\n",
-       " \n",
-       "          [[ 1.1493e-02,  6.0381e-03,  2.6160e-02],\n",
-       "           [ 2.2323e-02,  4.9374e-04,  1.0336e-02],\n",
-       "           [-1.2521e-04,  3.5427e-03, -8.2559e-04]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.3540e-02, -3.1304e-02, -1.2049e-02],\n",
-       "           [-1.3522e-02, -1.4369e-02,  1.4831e-02],\n",
-       "           [-5.6647e-03,  2.8068e-02,  1.2434e-02]],\n",
-       " \n",
-       "          [[-2.0231e-02, -3.8189e-03, -2.2149e-02],\n",
-       "           [-3.3457e-02, -2.9312e-02,  7.3416e-03],\n",
-       "           [-3.1276e-02, -4.9503e-02, -5.9178e-03]],\n",
-       " \n",
-       "          [[-2.0643e-02, -1.7179e-02, -3.1804e-02],\n",
-       "           [-1.6445e-02,  5.0633e-03,  9.8993e-03],\n",
-       "           [-1.0419e-02,  2.0037e-02,  3.8987e-04]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.2640e-02, -3.3205e-02, -4.4036e-03],\n",
-       "           [ 1.8503e-02, -1.5464e-02, -1.1134e-02],\n",
-       "           [ 9.3011e-04,  3.6451e-03,  1.1145e-02]],\n",
-       " \n",
-       "          [[-1.0027e-02, -1.1125e-02,  7.2101e-03],\n",
-       "           [ 8.5370e-03, -2.4245e-02, -1.7870e-02],\n",
-       "           [-1.2861e-02,  1.0968e-02,  2.6336e-03]],\n",
-       " \n",
-       "          [[ 4.2812e-02,  4.1831e-02,  1.7955e-02],\n",
-       "           [ 5.4603e-02,  2.4441e-02, -1.3003e-02],\n",
-       "           [ 5.4183e-03,  2.7678e-02,  1.9276e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.3172e-02,  3.1247e-02,  1.5246e-02],\n",
-       "           [ 1.6870e-02,  1.4777e-02,  1.6900e-02],\n",
-       "           [ 2.7611e-02, -3.1184e-03, -1.9656e-03]],\n",
-       " \n",
-       "          [[ 2.7945e-03,  2.3571e-02,  3.2599e-02],\n",
-       "           [-1.4418e-02,  3.1310e-02,  3.9025e-02],\n",
-       "           [-1.4201e-02,  2.2219e-02,  2.8432e-02]],\n",
-       " \n",
-       "          [[-2.1622e-03,  2.1338e-03,  1.9807e-02],\n",
-       "           [ 3.6813e-03,  4.1969e-04,  1.9940e-02],\n",
-       "           [ 2.3788e-02,  1.5245e-02,  3.4675e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 8.6672e-04,  1.7351e-02, -1.7232e-02],\n",
-       "           [-7.3278e-03,  3.6525e-02,  1.9066e-02],\n",
-       "           [-2.3444e-02, -1.9024e-02, -1.5144e-02]],\n",
-       " \n",
-       "          [[ 3.1922e-02,  5.8910e-02,  4.2894e-02],\n",
-       "           [ 3.7570e-02,  4.6937e-02,  4.4500e-02],\n",
-       "           [ 3.8033e-02,  2.5759e-02,  3.6761e-02]],\n",
-       " \n",
-       "          [[-4.8909e-03,  6.9769e-03, -8.5884e-03],\n",
-       "           [-2.7377e-03, -2.0894e-03,  1.8725e-02],\n",
-       "           [ 1.4032e-03,  2.0861e-02,  1.6670e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.3466, 0.3486, 0.2516, 0.2143, 0.3665, 0.2816, 0.4078, 0.2697, 0.2941,\n",
-       "         0.2541, 0.3787, 0.2500, 0.3116, 0.2320, 0.2836, 0.4045, 0.3472, 0.2817,\n",
-       "         0.3468, 0.2826, 0.2625, 0.2635, 0.2697, 0.2548, 0.3260, 0.2589, 0.2104,\n",
-       "         0.3416, 0.2156, 0.2737, 0.2658, 0.2617, 0.2879, 0.2930, 0.3007, 0.3115,\n",
-       "         0.2019, 0.2223, 0.2670, 0.3227, 0.2597, 0.2569, 0.1806, 0.2392, 0.3308,\n",
-       "         0.2528, 0.2735, 0.3274, 0.2362, 0.2259, 0.2330, 0.3230, 0.2545, 0.2217,\n",
-       "         0.3526, 0.2531, 0.2883, 0.2545, 0.3521, 0.2895, 0.2429, 0.3032, 0.2929,\n",
-       "         0.3468, 0.4236, 0.2031, 0.2981, 0.2246, 0.3334, 0.2568, 0.2960, 0.2479,\n",
-       "         0.2829, 0.3367, 0.2800, 0.2876, 0.2646, 0.2621, 0.1975, 0.2866, 0.2718,\n",
-       "         0.2696, 0.2833, 0.2506, 0.2549, 0.2526, 0.3420, 0.3241, 0.2588, 0.2879,\n",
-       "         0.2894, 0.2987, 0.2376, 0.2115, 0.2497, 0.2879, 0.2825, 0.3527, 0.2277,\n",
-       "         0.2439, 0.2466, 0.3670, 0.2794, 0.3059, 0.2454, 0.3211, 0.2284, 0.2658,\n",
-       "         0.2510, 0.2942, 0.3245, 0.3397, 0.2545, 0.3188, 0.2726, 0.2937, 0.2182,\n",
-       "         0.3240, 0.3914, 0.3833, 0.2456, 0.2216, 0.3378, 0.3226, 0.2693, 0.2288,\n",
-       "         0.3362, 0.2959, 0.3282, 0.2607, 0.3265, 0.3274, 0.3534, 0.2996, 0.3920,\n",
-       "         0.2158, 0.3085, 0.2778, 0.1640, 0.3091, 0.2926, 0.3303, 0.3143, 0.2359,\n",
-       "         0.5276, 0.3230, 0.3165, 0.4032, 0.2939, 0.2831, 0.2911, 0.4595, 0.2980,\n",
-       "         0.2834, 0.3165, 0.2981, 0.2997, 0.2429, 0.3310, 0.2652, 0.2983, 0.3406,\n",
-       "         0.4386, 0.2468, 0.3127, 0.3738, 0.3256, 0.2977, 0.3036, 0.2567, 0.3110,\n",
-       "         0.2933, 0.2872, 0.2596, 0.3186, 0.3093, 0.3336, 0.2659, 0.3169, 0.3835,\n",
-       "         0.2436, 0.2838, 0.2090, 0.2748, 0.3192, 0.2553, 0.3287, 0.3680, 0.3043,\n",
-       "         0.2889, 0.3092, 0.2844, 0.2120, 0.3067, 0.2409, 0.2993, 0.3445, 0.1605,\n",
-       "         0.4098, 0.2883, 0.2784, 0.2465, 0.1961, 0.2627, 0.2356, 0.2967, 0.2559,\n",
-       "         0.3266, 0.3167, 0.2816, 0.3016, 0.3005, 0.2912, 0.2797, 0.2813, 0.3100,\n",
-       "         0.2693, 0.3141, 0.2455, 0.2495, 0.3150, 0.3712, 0.2001, 0.3086, 0.2483,\n",
-       "         0.3046, 0.2873, 0.2278, 0.3173, 0.2662, 0.2622, 0.2261, 0.2806, 0.2026,\n",
-       "         0.2626, 0.2509, 0.2849, 0.3101, 0.2889, 0.3092, 0.2310, 0.2401, 0.2717,\n",
-       "         0.3212, 0.3114, 0.3791, 0.3168, 0.3641, 0.2536, 0.2217, 0.2638, 0.3285,\n",
-       "         0.2495, 0.2841, 0.2901, 0.1843], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.0979, -0.0783,  0.0691,  0.1583, -0.0239,  0.0665,  0.0525,  0.0523,\n",
-       "         -0.0100,  0.0526,  0.0613,  0.0009, -0.0521,  0.1209, -0.0360, -0.0587,\n",
-       "          0.0345,  0.0633, -0.0376,  0.0033,  0.0899,  0.1505, -0.0766,  0.0097,\n",
-       "         -0.0111,  0.0398,  0.0386,  0.1248,  0.0649, -0.0257,  0.0728,  0.0003,\n",
-       "          0.0779,  0.0311,  0.0198,  0.0755,  0.0359,  0.1105,  0.0555,  0.1162,\n",
-       "         -0.0106,  0.1936,  0.1788, -0.0080,  0.0157,  0.0852,  0.0095, -0.1222,\n",
-       "          0.0979,  0.0858,  0.0446, -0.0087,  0.0178,  0.0166, -0.0519,  0.0390,\n",
-       "         -0.0340,  0.0328,  0.0425,  0.0323,  0.0646,  0.0554,  0.0551, -0.0463,\n",
-       "         -0.1180,  0.0765,  0.0208,  0.0030, -0.0353,  0.0415,  0.0764,  0.0762,\n",
-       "          0.0418, -0.0260, -0.0478,  0.0043,  0.0061,  0.0526,  0.1252, -0.0472,\n",
-       "          0.0755, -0.0365,  0.0915,  0.0369,  0.0293,  0.0376,  0.0064, -0.0387,\n",
-       "          0.0161,  0.0116, -0.0280,  0.0354,  0.0885,  0.0998,  0.0147, -0.0637,\n",
-       "          0.0573, -0.0522,  0.1259,  0.1251,  0.0487, -0.0572,  0.0069, -0.0052,\n",
-       "          0.0361,  0.0153,  0.0215,  0.0030,  0.1218,  0.0622, -0.0162,  0.0250,\n",
-       "         -0.0141,  0.0307,  0.0555,  0.0352,  0.1161,  0.0005, -0.0585,  0.1104,\n",
-       "          0.0784, -0.0185,  0.0292,  0.0127,  0.0737,  0.1019, -0.0807,  0.1862,\n",
-       "          0.1132,  0.0155,  0.0459,  0.0069, -0.0113,  0.0079, -0.0205,  0.0757,\n",
-       "          0.1475,  0.0435,  0.1875, -0.0387, -0.0319, -0.0097, -0.0516,  0.0987,\n",
-       "         -0.1669, -0.0030, -0.0108,  0.0189, -0.0012,  0.1566, -0.0289, -0.0733,\n",
-       "         -0.0138,  0.1080,  0.0534,  0.0247, -0.0320,  0.1044, -0.0490, -0.0138,\n",
-       "         -0.0213, -0.0829, -0.1605,  0.0653,  0.0025, -0.0834, -0.0988,  0.0388,\n",
-       "          0.0077, -0.0400,  0.0012,  0.0498, -0.0587,  0.1521, -0.0002,  0.0586,\n",
-       "          0.0218, -0.0371,  0.0940, -0.0810, -0.0392,  0.1253,  0.1122,  0.0097,\n",
-       "          0.0147,  0.0310, -0.0931, -0.1875, -0.0218, -0.0553,  0.0199,  0.0252,\n",
-       "          0.0504,  0.1855,  0.0899,  0.0670, -0.0576,  0.0134,  0.0741,  0.0994,\n",
-       "         -0.0092,  0.0584,  0.0851,  0.0729,  0.0150,  0.0677,  0.0183, -0.0046,\n",
-       "          0.0578, -0.0340,  0.0119,  0.0014,  0.0884,  0.0227,  0.1103, -0.0407,\n",
-       "          0.0378, -0.0130,  0.1099,  0.1452, -0.0187, -0.1079,  0.1278,  0.0394,\n",
-       "          0.0046, -0.0248,  0.0078,  0.0367, -0.0322,  0.0021,  0.0185,  0.0055,\n",
-       "          0.0208,  0.0648, -0.1063,  0.0729,  0.1291,  0.0734, -0.0073,  0.0713,\n",
-       "          0.0854, -0.0501,  0.0240, -0.0014, -0.0177, -0.0436,  0.0270, -0.0634,\n",
-       "          0.1817,  0.0893,  0.0435, -0.0896,  0.0560,  0.0851,  0.0311,  0.0928],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-0.0282]],\n",
-       " \n",
-       "          [[ 0.0315]],\n",
-       " \n",
-       "          [[-0.0070]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0602]],\n",
-       " \n",
-       "          [[-0.0627]],\n",
-       " \n",
-       "          [[ 0.0324]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0343]],\n",
-       " \n",
-       "          [[-0.0014]],\n",
-       " \n",
-       "          [[ 0.0372]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0697]],\n",
-       " \n",
-       "          [[-0.0398]],\n",
-       " \n",
-       "          [[-0.0158]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0317]],\n",
-       " \n",
-       "          [[ 0.0017]],\n",
-       " \n",
-       "          [[ 0.0014]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0097]],\n",
-       " \n",
-       "          [[ 0.0500]],\n",
-       " \n",
-       "          [[ 0.0227]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0080]],\n",
-       " \n",
-       "          [[ 0.1207]],\n",
-       " \n",
-       "          [[ 0.0068]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0330]],\n",
-       " \n",
-       "          [[-0.0261]],\n",
-       " \n",
-       "          [[-0.0319]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0020]],\n",
-       " \n",
-       "          [[ 0.0022]],\n",
-       " \n",
-       "          [[ 0.0016]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0687]],\n",
-       " \n",
-       "          [[ 0.0082]],\n",
-       " \n",
-       "          [[-0.0408]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0231]],\n",
-       " \n",
-       "          [[ 0.1278]],\n",
-       " \n",
-       "          [[ 0.0158]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0036]],\n",
-       " \n",
-       "          [[-0.0336]],\n",
-       " \n",
-       "          [[-0.0534]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 1.3998e-01,  1.3448e-01,  5.7037e-02,  1.0168e-01,  1.2903e-01,\n",
-       "          1.2262e-01,  1.9085e-01,  1.3067e-01,  1.3123e-01,  1.6985e-01,\n",
-       "          9.5920e-02,  2.7333e-01,  1.9714e-01,  1.3528e-01,  1.8711e-01,\n",
-       "          1.9013e-01,  9.9214e-02,  1.4264e-01,  2.1424e-01,  1.6546e-01,\n",
-       "          1.8405e-01,  6.2783e-02,  8.6758e-02,  1.5176e-01,  6.5673e-02,\n",
-       "          2.0631e-01,  8.3615e-02,  1.1196e-01,  1.6708e-02,  1.9302e-01,\n",
-       "          1.4358e-01,  1.9931e-01,  1.0165e-01,  2.0198e-01,  1.0993e-01,\n",
-       "          1.6931e-01,  3.7817e-02,  9.8360e-02,  1.0569e-01,  1.0696e-01,\n",
-       "          1.7124e-01,  1.6245e-02,  7.5293e-02,  8.2569e-02,  8.6236e-02,\n",
-       "          1.1864e-01,  1.1565e-01,  1.5464e-01,  8.6908e-02,  1.3858e-01,\n",
-       "          1.0411e-01,  1.9311e-01,  1.2081e-01,  2.6116e-01,  2.2080e-01,\n",
-       "          9.0266e-02,  2.0011e-01,  1.6799e-01,  2.2388e-02,  1.7756e-01,\n",
-       "          1.7045e-02,  1.7456e-01,  9.7776e-02,  1.7903e-01,  3.4130e-01,\n",
-       "          4.5595e-02,  2.0565e-01,  1.7821e-01,  1.1829e-01,  2.0702e-01,\n",
-       "          9.9615e-02,  1.5321e-01,  1.4612e-01,  2.1762e-01,  1.9869e-01,\n",
-       "          1.4822e-01,  1.7887e-01,  1.3811e-01,  7.0329e-02,  1.0219e-01,\n",
-       "          7.7786e-02,  1.4564e-01,  6.1735e-02,  2.4624e-01,  5.6980e-02,\n",
-       "          2.4649e-01,  1.4997e-01,  1.2023e-01,  9.3563e-02,  1.6895e-01,\n",
-       "          1.1699e-01,  1.1628e-01,  8.9709e-02,  1.6683e-01,  1.9800e-01,\n",
-       "          1.1510e-01,  1.8243e-01,  1.0303e-01,  1.1668e-01,  9.8250e-02,\n",
-       "          1.5648e-01,  2.4797e-01,  1.0790e-01,  1.2600e-01,  2.9779e-02,\n",
-       "          3.3730e-02,  1.1640e-01,  2.0892e-01,  8.9628e-02,  2.6953e-01,\n",
-       "          1.3295e-01,  1.0177e-01,  9.1125e-02,  1.0303e-01,  8.7288e-02,\n",
-       "          1.0784e-01,  1.0285e-01,  1.9883e-01,  2.5447e-01,  9.8214e-02,\n",
-       "          1.7961e-01,  3.8666e-02,  1.3832e-01,  1.4707e-01,  1.7130e-01,\n",
-       "          7.9865e-02,  1.5177e-01,  5.9522e-02,  1.0328e-01,  1.1633e-01,\n",
-       "          2.4162e-01,  1.9808e-01,  1.8403e-01,  9.3200e-02,  1.6415e-01,\n",
-       "          1.0428e-01,  5.7308e-02,  2.1066e-01,  7.6313e-02,  1.8411e-01,\n",
-       "          1.4266e-01,  2.1187e-01,  2.2867e-01,  9.3983e-02,  2.6160e-01,\n",
-       "          8.1211e-02,  1.0688e-01,  2.0277e-01,  1.2851e-01,  4.5618e-02,\n",
-       "          1.3915e-01,  1.8309e-01,  1.8060e-01,  1.7245e-01,  1.6430e-02,\n",
-       "          1.4881e-01,  6.4883e-02,  1.2441e-01,  1.0066e-01,  5.1877e-02,\n",
-       "          1.8743e-01,  2.0115e-01,  1.9944e-01,  9.9107e-02,  8.3722e-02,\n",
-       "          9.4250e-02,  1.0773e-01,  8.7141e-02,  1.2827e-01,  2.1504e-01,\n",
-       "          1.3961e-01,  1.7610e-01,  1.2790e-01,  6.0716e-02,  1.2596e-01,\n",
-       "          1.5885e-01,  1.1667e-01,  1.4740e-01,  7.2317e-02,  1.6323e-01,\n",
-       "          8.8816e-02,  4.5572e-03,  8.5809e-02,  1.9771e-01,  1.7412e-01,\n",
-       "          9.4790e-02,  1.3854e-01,  1.8236e-01,  1.9537e-01,  1.7318e-01,\n",
-       "          1.4233e-01,  1.4353e-01,  2.0672e-01,  7.7013e-02,  1.1770e-01,\n",
-       "         -1.0953e-05,  1.4953e-01,  4.7984e-02,  1.8016e-01,  7.9586e-02,\n",
-       "          1.3471e-01,  1.6576e-01,  8.0412e-02,  1.1837e-01,  1.8777e-01,\n",
-       "          6.4029e-02,  1.4280e-01,  1.4180e-01,  1.3355e-01,  1.1791e-01,\n",
-       "          1.7278e-01,  1.1442e-01,  1.4765e-01,  1.1812e-01,  1.4531e-01,\n",
-       "          1.2207e-01,  4.9054e-02,  1.8204e-01,  1.5672e-01,  5.7513e-02,\n",
-       "          9.8382e-02,  1.8274e-01,  1.2996e-01,  1.6303e-01,  1.3481e-01,\n",
-       "          2.2216e-01,  1.8005e-01,  7.9984e-02,  1.1020e-01,  8.4676e-02,\n",
-       "          1.2890e-01,  5.8154e-02,  1.8113e-01,  8.8887e-02,  1.1053e-01,\n",
-       "          1.2525e-01,  6.6904e-02,  1.6194e-01,  6.3338e-02,  1.3653e-01,\n",
-       "          1.3734e-01,  6.2315e-02,  1.0484e-01,  3.2640e-02,  2.3100e-01,\n",
-       "          1.5250e-01,  1.2770e-01,  2.4847e-01,  1.3564e-01,  1.1580e-01,\n",
-       "          1.3215e-01,  1.0948e-01,  1.8043e-01,  1.6041e-01,  2.1941e-01,\n",
-       "          6.3498e-02], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.0979, -0.0783,  0.0691,  0.1583, -0.0239,  0.0665,  0.0525,  0.0523,\n",
-       "         -0.0100,  0.0526,  0.0613,  0.0009, -0.0521,  0.1209, -0.0360, -0.0587,\n",
-       "          0.0345,  0.0633, -0.0376,  0.0033,  0.0899,  0.1505, -0.0766,  0.0097,\n",
-       "         -0.0111,  0.0398,  0.0386,  0.1248,  0.0649, -0.0257,  0.0728,  0.0003,\n",
-       "          0.0779,  0.0311,  0.0198,  0.0755,  0.0359,  0.1105,  0.0555,  0.1162,\n",
-       "         -0.0106,  0.1936,  0.1788, -0.0080,  0.0157,  0.0852,  0.0095, -0.1222,\n",
-       "          0.0979,  0.0858,  0.0446, -0.0087,  0.0178,  0.0166, -0.0519,  0.0390,\n",
-       "         -0.0340,  0.0328,  0.0425,  0.0323,  0.0646,  0.0554,  0.0551, -0.0463,\n",
-       "         -0.1180,  0.0765,  0.0208,  0.0030, -0.0353,  0.0415,  0.0764,  0.0762,\n",
-       "          0.0418, -0.0260, -0.0478,  0.0043,  0.0061,  0.0526,  0.1252, -0.0472,\n",
-       "          0.0755, -0.0365,  0.0915,  0.0369,  0.0293,  0.0376,  0.0064, -0.0387,\n",
-       "          0.0161,  0.0116, -0.0280,  0.0354,  0.0885,  0.0998,  0.0147, -0.0637,\n",
-       "          0.0573, -0.0522,  0.1259,  0.1251,  0.0487, -0.0572,  0.0069, -0.0052,\n",
-       "          0.0361,  0.0153,  0.0215,  0.0030,  0.1218,  0.0622, -0.0162,  0.0250,\n",
-       "         -0.0141,  0.0307,  0.0555,  0.0352,  0.1161,  0.0005, -0.0585,  0.1104,\n",
-       "          0.0784, -0.0185,  0.0292,  0.0127,  0.0737,  0.1019, -0.0807,  0.1862,\n",
-       "          0.1132,  0.0155,  0.0459,  0.0069, -0.0113,  0.0079, -0.0205,  0.0757,\n",
-       "          0.1475,  0.0435,  0.1875, -0.0387, -0.0319, -0.0097, -0.0516,  0.0987,\n",
-       "         -0.1669, -0.0030, -0.0108,  0.0189, -0.0012,  0.1566, -0.0289, -0.0733,\n",
-       "         -0.0138,  0.1080,  0.0534,  0.0247, -0.0320,  0.1044, -0.0490, -0.0138,\n",
-       "         -0.0213, -0.0829, -0.1605,  0.0653,  0.0025, -0.0834, -0.0988,  0.0388,\n",
-       "          0.0077, -0.0400,  0.0012,  0.0498, -0.0587,  0.1521, -0.0002,  0.0586,\n",
-       "          0.0218, -0.0371,  0.0940, -0.0810, -0.0392,  0.1253,  0.1122,  0.0097,\n",
-       "          0.0147,  0.0310, -0.0931, -0.1875, -0.0218, -0.0553,  0.0199,  0.0252,\n",
-       "          0.0504,  0.1855,  0.0899,  0.0670, -0.0576,  0.0134,  0.0741,  0.0994,\n",
-       "         -0.0092,  0.0584,  0.0851,  0.0729,  0.0150,  0.0677,  0.0183, -0.0046,\n",
-       "          0.0578, -0.0340,  0.0119,  0.0014,  0.0884,  0.0227,  0.1103, -0.0407,\n",
-       "          0.0378, -0.0130,  0.1099,  0.1452, -0.0187, -0.1079,  0.1278,  0.0394,\n",
-       "          0.0046, -0.0248,  0.0078,  0.0367, -0.0322,  0.0021,  0.0185,  0.0055,\n",
-       "          0.0208,  0.0648, -0.1063,  0.0729,  0.1291,  0.0734, -0.0073,  0.0713,\n",
-       "          0.0854, -0.0501,  0.0240, -0.0014, -0.0177, -0.0436,  0.0270, -0.0634,\n",
-       "          0.1817,  0.0893,  0.0435, -0.0896,  0.0560,  0.0851,  0.0311,  0.0928],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-5.3838e-04, -1.1527e-02, -1.5879e-02],\n",
-       "           [ 5.4532e-03,  6.1199e-05, -2.4640e-03],\n",
-       "           [-8.1678e-03,  1.3048e-03, -1.5022e-02]],\n",
-       " \n",
-       "          [[ 1.3511e-04, -3.4285e-03,  7.5542e-03],\n",
-       "           [ 1.3700e-02,  1.4563e-02,  1.6203e-02],\n",
-       "           [ 1.8983e-02,  9.2408e-03,  1.6350e-02]],\n",
-       " \n",
-       "          [[-2.1057e-02, -3.2673e-05, -1.0758e-03],\n",
-       "           [-1.0970e-02, -8.6525e-04,  2.5687e-03],\n",
-       "           [-1.0018e-02, -5.8712e-03, -1.2089e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 5.3630e-03,  7.3538e-03, -4.8232e-03],\n",
-       "           [ 1.6509e-02,  4.1262e-02,  1.6288e-02],\n",
-       "           [ 9.9767e-03,  3.5593e-02,  2.5516e-02]],\n",
-       " \n",
-       "          [[-1.5146e-02, -9.3630e-03,  1.6061e-03],\n",
-       "           [-2.3884e-02, -6.8738e-03, -2.7217e-03],\n",
-       "           [-2.0966e-02, -2.0591e-03, -6.0423e-03]],\n",
-       " \n",
-       "          [[ 4.2047e-03,  8.4443e-03,  1.1156e-02],\n",
-       "           [ 3.2888e-03,  1.3060e-02,  1.8571e-02],\n",
-       "           [-1.0119e-02, -1.1345e-02, -2.1450e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-3.9021e-03,  1.3249e-03,  1.1836e-02],\n",
-       "           [-9.5135e-03,  1.8366e-02,  4.1317e-02],\n",
-       "           [-1.0952e-02, -1.3443e-02, -1.0975e-02]],\n",
-       " \n",
-       "          [[ 1.6552e-02,  2.3222e-03, -7.4879e-03],\n",
-       "           [ 1.4309e-03, -9.5436e-03, -7.1289e-03],\n",
-       "           [ 1.3348e-02,  1.5970e-02,  2.3107e-02]],\n",
-       " \n",
-       "          [[-5.4555e-03, -8.9057e-04, -1.2589e-02],\n",
-       "           [ 5.0956e-03,  2.2157e-03, -9.7308e-03],\n",
-       "           [-1.4579e-02, -2.0585e-02, -2.3853e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.9985e-02, -5.2413e-03, -1.5206e-03],\n",
-       "           [-1.6919e-02,  1.2072e-02, -1.1599e-02],\n",
-       "           [-1.3012e-02,  9.7941e-03, -7.2316e-03]],\n",
-       " \n",
-       "          [[ 9.1617e-03,  2.5465e-02,  1.5420e-02],\n",
-       "           [ 2.6022e-02,  3.5331e-02,  6.1584e-03],\n",
-       "           [ 2.4861e-02,  2.9241e-02,  1.5503e-02]],\n",
-       " \n",
-       "          [[ 4.9573e-02,  4.4685e-02,  4.2367e-02],\n",
-       "           [ 4.5786e-02,  3.7375e-02,  3.3574e-02],\n",
-       "           [ 5.4500e-02,  4.6305e-02,  5.2398e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-2.1007e-03,  3.6161e-03,  2.0132e-02],\n",
-       "           [ 6.0481e-03,  3.2565e-03,  4.4267e-03],\n",
-       "           [-9.9514e-03, -1.3001e-02, -2.1497e-03]],\n",
-       " \n",
-       "          [[ 9.2300e-03,  7.7519e-03,  8.3948e-03],\n",
-       "           [ 9.3227e-03,  1.5556e-02,  3.4574e-03],\n",
-       "           [ 1.8407e-02,  1.4975e-02,  5.0501e-03]],\n",
-       " \n",
-       "          [[-6.3876e-04, -1.3853e-02, -1.8190e-03],\n",
-       "           [ 1.6715e-02,  3.8238e-03,  9.1349e-03],\n",
-       "           [ 1.3911e-02,  4.9178e-03,  3.2892e-04]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.9876e-02, -1.5583e-02,  1.4602e-02],\n",
-       "           [-4.5596e-04,  2.1069e-04,  1.4941e-03],\n",
-       "           [ 1.9728e-02,  5.4763e-02,  3.4454e-02]],\n",
-       " \n",
-       "          [[-6.3491e-03,  1.2846e-02, -7.6220e-03],\n",
-       "           [-3.4666e-03,  4.5488e-04,  1.5783e-03],\n",
-       "           [-4.1686e-02, -5.8847e-02, -4.3132e-02]],\n",
-       " \n",
-       "          [[ 1.0441e-02,  1.9804e-02,  3.9145e-03],\n",
-       "           [ 9.4580e-03,  1.6469e-02,  5.7090e-05],\n",
-       "           [ 1.6160e-02,  1.8821e-02,  7.2974e-03]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 4.4683e-03, -4.6239e-03, -1.2887e-02],\n",
-       "           [ 1.1484e-02,  5.2812e-03, -1.2919e-02],\n",
-       "           [ 8.6018e-03,  1.5455e-03, -2.6427e-03]],\n",
-       " \n",
-       "          [[ 1.5076e-02,  3.4337e-03,  5.2494e-03],\n",
-       "           [ 2.5450e-02,  1.6346e-02,  1.7715e-02],\n",
-       "           [ 1.4780e-02,  2.3915e-02,  3.3652e-02]],\n",
-       " \n",
-       "          [[ 1.2632e-02,  1.3881e-02,  1.3911e-02],\n",
-       "           [ 2.0135e-03, -7.0285e-03,  5.3680e-03],\n",
-       "           [ 5.6748e-03,  1.1028e-02,  1.2488e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.1492e-02,  1.4633e-02, -6.0709e-03],\n",
-       "           [ 3.9390e-03,  1.4357e-02,  3.7170e-03],\n",
-       "           [-1.4616e-02, -8.0252e-03,  3.0921e-03]],\n",
-       " \n",
-       "          [[ 3.3918e-02,  1.3844e-02,  2.1488e-02],\n",
-       "           [ 6.5221e-03, -1.3726e-02,  2.9260e-03],\n",
-       "           [ 1.8505e-02,  6.7748e-03, -1.9349e-03]],\n",
-       " \n",
-       "          [[-1.2339e-02, -2.0768e-02, -2.3024e-02],\n",
-       "           [-1.9164e-02, -1.8214e-02, -1.8232e-02],\n",
-       "           [-1.8870e-02, -1.4949e-02, -1.7933e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-6.1257e-04, -1.6341e-03,  5.4617e-03],\n",
-       "           [-1.1883e-02, -7.0983e-03, -1.0125e-02],\n",
-       "           [ 2.9166e-02,  3.2148e-02, -5.3870e-03]],\n",
-       " \n",
-       "          [[-8.3748e-03, -1.3325e-02, -2.6637e-02],\n",
-       "           [-1.0294e-02, -1.9187e-02, -2.1893e-02],\n",
-       "           [-1.2139e-02, -1.4033e-02, -2.3464e-02]],\n",
-       " \n",
-       "          [[ 1.3865e-02,  1.8356e-02, -1.1324e-03],\n",
-       "           [-7.6168e-03,  3.5782e-04, -4.4800e-05],\n",
-       "           [-3.7866e-02, -3.3161e-02, -1.9761e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.0676e-02, -3.1363e-03, -8.5802e-03],\n",
-       "           [-1.3171e-02,  9.9575e-03, -1.6122e-03],\n",
-       "           [ 5.5101e-03,  1.3935e-02, -5.7697e-03]],\n",
-       " \n",
-       "          [[ 1.1125e-02,  1.9404e-02,  6.1532e-03],\n",
-       "           [-2.1732e-02,  1.3041e-02,  1.5313e-02],\n",
-       "           [-2.7787e-02, -1.5592e-02, -2.0657e-03]],\n",
-       " \n",
-       "          [[ 1.4011e-02,  6.0794e-03,  9.1467e-03],\n",
-       "           [-8.4015e-04,  1.7289e-03,  1.5674e-02],\n",
-       "           [-7.8754e-03, -1.8084e-03,  1.3104e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-4.6308e-03, -1.7622e-02,  2.2715e-04],\n",
-       "           [ 5.1224e-03, -1.1579e-03,  1.8967e-02],\n",
-       "           [ 4.1956e-04, -2.0176e-02, -6.7084e-03]],\n",
-       " \n",
-       "          [[-2.4629e-03, -1.6900e-03, -6.0429e-03],\n",
-       "           [-1.0407e-02,  1.3464e-03, -2.1995e-03],\n",
-       "           [ 9.2197e-03,  5.0820e-03, -1.4829e-02]],\n",
-       " \n",
-       "          [[-4.4680e-03, -8.8429e-03, -1.3907e-02],\n",
-       "           [ 2.1747e-02, -9.3993e-03, -2.3542e-02],\n",
-       "           [ 1.6626e-02, -1.8721e-02, -2.4806e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.3611e-03, -3.9266e-03,  3.7355e-03],\n",
-       "           [ 1.3347e-02,  1.2434e-02, -9.4425e-03],\n",
-       "           [ 2.1499e-02, -3.4208e-03,  3.7403e-03]],\n",
-       " \n",
-       "          [[-1.8244e-02, -1.9023e-03,  2.1449e-03],\n",
-       "           [-2.2189e-02,  7.3349e-03, -7.2506e-04],\n",
-       "           [-2.3303e-02,  1.2685e-03,  1.3029e-02]],\n",
-       " \n",
-       "          [[-9.7367e-03, -1.0875e-02, -2.3729e-02],\n",
-       "           [ 4.3489e-03,  1.0877e-03, -8.2711e-03],\n",
-       "           [-1.7941e-02, -1.8486e-02, -1.3773e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2231, 0.2118, 0.2097, 0.2112, 0.2113, 0.2145, 0.2214, 0.2439, 0.2158,\n",
-       "         0.2454, 0.2181, 0.2258, 0.2099, 0.2315, 0.2133, 0.2740, 0.2406, 0.2098,\n",
-       "         0.1963, 0.2280, 0.2169, 0.2188, 0.2904, 0.2714, 0.2112, 0.2276, 0.1890,\n",
-       "         0.1301, 0.2447, 0.2610, 0.2088, 0.2707, 0.2528, 0.2379, 0.2973, 0.2517,\n",
-       "         0.2094, 0.2388, 0.2706, 0.2363, 0.2207, 0.1838, 0.2529, 0.2107, 0.2173,\n",
-       "         0.2566, 0.2598, 0.2870, 0.1881, 0.2322, 0.2256, 0.2122, 0.2454, 0.2484,\n",
-       "         0.1960, 0.2584, 0.2890, 0.2741, 0.2095, 0.2196, 0.2399, 0.2642, 0.1616,\n",
-       "         0.2693, 0.2796, 0.2325, 0.2162, 0.2285, 0.2312, 0.2089, 0.2289, 0.2184,\n",
-       "         0.1968, 0.2317, 0.2089, 0.2608, 0.3180, 0.2833, 0.2423, 0.1707, 0.2186,\n",
-       "         0.2222, 0.2125, 0.2151, 0.1804, 0.2171, 0.2639, 0.2692, 0.2715, 0.2170,\n",
-       "         0.2228, 0.2060, 0.2704, 0.2493, 0.2409, 0.2291, 0.2903, 0.2898, 0.1838,\n",
-       "         0.2082, 0.2429, 0.1864, 0.1935, 0.2269, 0.1994, 0.2472, 0.2670, 0.2819,\n",
-       "         0.2277, 0.2174, 0.2139, 0.2336, 0.2648, 0.1874, 0.2860, 0.2578, 0.3631,\n",
-       "         0.2221, 0.2024, 0.2161, 0.1916, 0.2196, 0.2809, 0.2504, 0.2463, 0.2432,\n",
-       "         0.2495, 0.2567, 0.2437, 0.1696, 0.2177, 0.2661, 0.2200, 0.2465, 0.2937,\n",
-       "         0.2368, 0.2121, 0.2244, 0.2158, 0.2733, 0.2474, 0.2125, 0.2828, 0.2899,\n",
-       "         0.2068, 0.2067, 0.2730, 0.1935, 0.1858, 0.2215, 0.2120, 0.2401, 0.2368,\n",
-       "         0.2557, 0.2389, 0.2267, 0.2553, 0.2008, 0.2213, 0.3231, 0.2597, 0.2020,\n",
-       "         0.2258, 0.2361, 0.2646, 0.2377, 0.2129, 0.2342, 0.3070, 0.2165, 0.1980,\n",
-       "         0.2788, 0.2233, 0.2033, 0.2676, 0.2633, 0.3589, 0.2389, 0.2615, 0.2235,\n",
-       "         0.2073, 0.2519, 0.1859, 0.2026, 0.2306, 0.2528, 0.2550, 0.2305, 0.2300,\n",
-       "         0.2172, 0.1993, 0.2604, 0.2925, 0.2163, 0.2004, 0.1971, 0.2076, 0.1957,\n",
-       "         0.2530, 0.1662, 0.1908, 0.2378, 0.2162, 0.2546, 0.2061, 0.2327, 0.2670,\n",
-       "         0.2174, 0.1896, 0.2477, 0.2305, 0.1986, 0.2394, 0.2498, 0.2144, 0.1821,\n",
-       "         0.2400, 0.2191, 0.2065, 0.2098, 0.2961, 0.1884, 0.2167, 0.2102, 0.1883,\n",
-       "         0.2600, 0.2637, 0.2376, 0.2684, 0.2523, 0.2020, 0.2269, 0.1922, 0.2297,\n",
-       "         0.2471, 0.2259, 0.1908, 0.2065, 0.2536, 0.2127, 0.2013, 0.2147, 0.1950,\n",
-       "         0.2717, 0.2784, 0.2266, 0.1953, 0.2614, 0.2255, 0.2131, 0.2341, 0.2210,\n",
-       "         0.2014, 0.2035, 0.2694, 0.1958], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.1843, -0.1631, -0.1616, -0.1845, -0.0701, -0.1997, -0.1980, -0.1474,\n",
-       "         -0.0847, -0.1639, -0.1389, -0.0972, -0.1315, -0.1600, -0.1939, -0.2248,\n",
-       "         -0.1739, -0.0968, -0.1564, -0.2251, -0.2580, -0.1729, -0.2324, -0.2143,\n",
-       "         -0.0713, -0.1509, -0.0373,  0.0913, -0.1734, -0.2047, -0.0931, -0.2181,\n",
-       "         -0.1742, -0.3401, -0.3026, -0.0701, -0.1463, -0.1365, -0.2338, -0.1394,\n",
-       "         -0.2059, -0.0783, -0.1649, -0.1263, -0.1709, -0.1264, -0.1805, -0.2210,\n",
-       "         -0.1061, -0.1355, -0.1896, -0.2516, -0.1830, -0.1759, -0.1510, -0.2365,\n",
-       "         -0.2794, -0.1831, -0.1168, -0.1578, -0.1735, -0.1821, -0.0747, -0.2526,\n",
-       "         -0.2918, -0.2336, -0.2041, -0.1421, -0.2226, -0.0937, -0.1884, -0.2111,\n",
-       "         -0.1006, -0.1913, -0.2042, -0.1831, -0.1694, -0.1971, -0.2734, -0.1553,\n",
-       "         -0.2078, -0.0921, -0.0981, -0.2652, -0.0683, -0.1245, -0.1930, -0.1971,\n",
-       "         -0.2775, -0.1109, -0.1388, -0.1762, -0.2659, -0.1255, -0.2237, -0.1478,\n",
-       "         -0.2800, -0.2567, -0.1722, -0.1695, -0.1705, -0.0784, -0.1172, -0.1198,\n",
-       "         -0.1340, -0.1185, -0.2015, -0.2932, -0.1482, -0.2023, -0.1443, -0.1084,\n",
-       "         -0.3988, -0.0837, -0.3552, -0.2274, -0.3472, -0.1271, -0.0938, -0.2798,\n",
-       "         -0.1096, -0.1577, -0.1896, -0.1995, -0.2428, -0.1211, -0.1853, -0.2070,\n",
-       "         -0.1655, -0.1307, -0.2224, -0.3050, -0.2436, -0.2599, -0.2427, -0.2304,\n",
-       "         -0.1230, -0.0804, -0.2151, -0.1943, -0.1424, -0.1229, -0.2492, -0.2033,\n",
-       "         -0.0829, -0.0936, -0.1890, -0.1908, -0.1254, -0.1280, -0.1095, -0.1497,\n",
-       "         -0.2009, -0.2208, -0.1631, -0.2538, -0.1849, -0.1237, -0.2011, -0.2899,\n",
-       "         -0.1928, -0.1778, -0.1305, -0.2441, -0.2124, -0.3208, -0.1713, -0.1181,\n",
-       "         -0.3194, -0.1917, -0.1800, -0.1726, -0.1704, -0.1202, -0.2083, -0.1699,\n",
-       "         -0.2553, -0.1533, -0.2885, -0.2192, -0.1757, -0.1675, -0.1301, -0.1115,\n",
-       "         -0.1938, -0.2252, -0.1928, -0.1638, -0.1147, -0.2161, -0.1114, -0.1783,\n",
-       "         -0.2235, -0.2082, -0.0988, -0.0810, -0.2072, -0.0714, -0.1993, -0.0716,\n",
-       "         -0.1160, -0.1601, -0.1676, -0.1135, -0.1582, -0.1490, -0.2266, -0.1096,\n",
-       "         -0.0904, -0.1520, -0.2329, -0.1149, -0.2544, -0.2293, -0.1073, -0.0329,\n",
-       "         -0.1759, -0.2161, -0.1277, -0.1233, -0.2389, -0.0564, -0.0931, -0.1395,\n",
-       "         -0.1537, -0.1703, -0.1671, -0.1563, -0.2416, -0.2520, -0.1126, -0.0863,\n",
-       "         -0.1495, -0.1849, -0.2018, -0.1035, -0.1585, -0.1429, -0.1633, -0.1850,\n",
-       "         -0.1234, -0.1132, -0.0991, -0.3069, -0.2873, -0.1989, -0.1604, -0.2066,\n",
-       "         -0.0939, -0.1324, -0.1641, -0.1179, -0.1178, -0.1556, -0.2454, -0.0840],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 8.3994e-04,  7.7100e-03,  1.7529e-02],\n",
-       "           [-1.3136e-02, -1.3433e-02, -1.0733e-02],\n",
-       "           [-5.5611e-04,  7.0798e-03,  1.0499e-02]],\n",
-       " \n",
-       "          [[-1.3136e-02, -3.0884e-02, -1.7931e-02],\n",
-       "           [ 1.1663e-03, -5.9801e-03, -4.9453e-03],\n",
-       "           [ 1.6389e-02,  2.0018e-02,  2.0692e-02]],\n",
-       " \n",
-       "          [[-1.3429e-02,  4.5801e-04,  8.8805e-03],\n",
-       "           [ 5.2391e-03, -2.2173e-04,  5.1206e-03],\n",
-       "           [-2.5825e-02, -2.3561e-02, -7.1251e-04]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-3.8071e-03, -9.5845e-03,  1.0660e-02],\n",
-       "           [ 8.9454e-03, -1.5041e-02,  3.8024e-03],\n",
-       "           [-1.9412e-02, -1.7794e-02,  4.7761e-03]],\n",
-       " \n",
-       "          [[ 5.3028e-04,  4.3563e-03,  1.4286e-02],\n",
-       "           [-2.3311e-02,  3.8197e-03,  1.0576e-02],\n",
-       "           [-1.8255e-02, -1.9401e-02, -1.1005e-02]],\n",
-       " \n",
-       "          [[-3.1573e-02, -1.3919e-02, -3.7889e-02],\n",
-       "           [-3.5850e-02, -1.0875e-02,  1.2829e-02],\n",
-       "           [-4.3214e-03,  1.7770e-02,  3.0186e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.5104e-02,  2.5964e-03,  6.9159e-04],\n",
-       "           [-2.3776e-02, -1.1124e-02, -1.2865e-02],\n",
-       "           [-1.7888e-02, -1.9266e-02, -1.4817e-02]],\n",
-       " \n",
-       "          [[-9.2737e-04,  4.9655e-03,  2.4060e-02],\n",
-       "           [ 7.2476e-03,  6.5797e-03,  1.3477e-02],\n",
-       "           [ 6.3269e-03,  1.6732e-02,  2.2035e-02]],\n",
-       " \n",
-       "          [[-6.4670e-03, -6.5889e-03, -5.5316e-04],\n",
-       "           [ 7.4894e-04,  5.7706e-03,  5.0750e-03],\n",
-       "           [-7.7773e-03,  8.9526e-03,  1.1737e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.1511e-02,  3.1796e-02,  1.9539e-02],\n",
-       "           [ 4.1989e-02,  3.7677e-02,  1.9810e-02],\n",
-       "           [ 2.2758e-02,  1.9959e-02,  1.4732e-02]],\n",
-       " \n",
-       "          [[ 4.9028e-03,  2.8414e-03, -5.2479e-04],\n",
-       "           [ 2.3183e-02,  5.8282e-04, -1.5102e-02],\n",
-       "           [ 9.8088e-03, -1.6444e-03, -1.5134e-02]],\n",
-       " \n",
-       "          [[-1.1330e-02, -1.5663e-02, -3.0370e-02],\n",
-       "           [-1.0447e-02, -8.6946e-03, -2.3512e-02],\n",
-       "           [ 7.1688e-03, -2.2442e-02, -3.2668e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 4.4154e-03, -5.1521e-03,  2.2567e-03],\n",
-       "           [ 1.9308e-02,  1.7150e-02,  1.4005e-02],\n",
-       "           [ 2.2950e-04, -2.0745e-03, -2.5378e-03]],\n",
-       " \n",
-       "          [[-1.8673e-02, -2.0811e-02, -2.7456e-02],\n",
-       "           [-8.6851e-03, -9.9567e-03, -1.8341e-02],\n",
-       "           [-3.8170e-03,  1.3766e-03, -8.8911e-03]],\n",
-       " \n",
-       "          [[-1.9593e-02, -1.3096e-02, -1.2748e-02],\n",
-       "           [-6.4971e-03,  1.0629e-02,  1.4226e-02],\n",
-       "           [ 5.6355e-03,  5.6472e-03, -4.3750e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.7803e-03, -4.6260e-03, -1.1056e-02],\n",
-       "           [-2.1395e-02, -9.5772e-03,  5.2178e-03],\n",
-       "           [-6.2604e-03, -1.5297e-02, -1.2575e-03]],\n",
-       " \n",
-       "          [[ 2.3156e-02,  2.5939e-02,  1.4578e-02],\n",
-       "           [ 9.9940e-03,  2.3776e-03,  5.7083e-03],\n",
-       "           [-3.7721e-03, -1.7526e-02,  1.1601e-03]],\n",
-       " \n",
-       "          [[-1.0199e-02, -4.8811e-03,  7.8451e-03],\n",
-       "           [ 3.0686e-03, -9.4534e-04,  1.5031e-02],\n",
-       "           [ 1.4332e-02,  3.8354e-03,  8.7141e-03]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 2.7704e-03, -3.8391e-03, -1.4080e-02],\n",
-       "           [ 1.2259e-02, -8.0735e-04, -1.3934e-02],\n",
-       "           [ 1.7048e-02,  1.3694e-02,  1.0892e-02]],\n",
-       " \n",
-       "          [[-2.7665e-03,  5.0795e-03,  2.1059e-02],\n",
-       "           [-8.3471e-03, -9.6370e-03,  2.2098e-02],\n",
-       "           [ 1.7648e-02,  1.7523e-02,  3.2837e-02]],\n",
-       " \n",
-       "          [[ 6.6748e-03,  6.7010e-04, -2.9129e-02],\n",
-       "           [-2.9792e-02, -3.5646e-02, -1.9422e-02],\n",
-       "           [-4.0545e-02, -4.7741e-02, -2.6523e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-5.8995e-03,  1.3936e-02,  9.3173e-03],\n",
-       "           [ 2.0731e-02,  1.0108e-02,  1.2778e-03],\n",
-       "           [-6.1233e-03, -8.7782e-03,  6.4701e-03]],\n",
-       " \n",
-       "          [[-1.9601e-02, -7.2193e-03, -9.5388e-03],\n",
-       "           [-1.2271e-05, -8.1983e-03, -1.1848e-02],\n",
-       "           [ 7.0126e-03,  1.1559e-02,  9.9933e-04]],\n",
-       " \n",
-       "          [[ 2.9105e-02, -6.4379e-03, -4.8290e-03],\n",
-       "           [ 2.3580e-02, -1.8364e-02,  4.6473e-04],\n",
-       "           [ 3.4228e-02,  1.0586e-02, -1.7324e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.9031e-02, -1.4550e-02, -1.5745e-02],\n",
-       "           [-3.4789e-02, -3.7621e-02, -1.8219e-02],\n",
-       "           [-4.7587e-02, -4.1431e-02, -2.3431e-02]],\n",
-       " \n",
-       "          [[ 2.6594e-04, -3.8479e-03,  3.3691e-04],\n",
-       "           [ 8.3023e-03, -7.9874e-04, -7.6274e-03],\n",
-       "           [ 2.0648e-02,  2.5605e-02,  7.9960e-03]],\n",
-       " \n",
-       "          [[-2.1270e-02, -1.9580e-02,  9.0429e-04],\n",
-       "           [-7.1257e-03,  1.7957e-02,  1.6296e-02],\n",
-       "           [-3.9863e-03,  3.0529e-02,  1.8478e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 4.7240e-03, -3.0108e-03,  1.7133e-02],\n",
-       "           [ 6.2567e-03, -1.4469e-03,  1.9600e-02],\n",
-       "           [ 5.1401e-03, -8.9301e-03,  4.2081e-03]],\n",
-       " \n",
-       "          [[ 5.1442e-04, -4.8339e-03,  1.1181e-02],\n",
-       "           [-1.7255e-03, -1.1838e-03,  1.0798e-02],\n",
-       "           [-2.3416e-02, -1.5284e-02, -5.4657e-03]],\n",
-       " \n",
-       "          [[-1.2281e-02, -7.8411e-03,  2.1424e-02],\n",
-       "           [ 3.3753e-03,  5.9411e-03,  2.4748e-02],\n",
-       "           [ 2.3185e-02,  5.8707e-03,  1.3400e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 2.1079e-02,  2.1042e-02,  1.1727e-02],\n",
-       "           [ 2.2583e-02,  2.1820e-02,  1.5952e-02],\n",
-       "           [ 2.3683e-02,  2.9359e-02,  2.1271e-02]],\n",
-       " \n",
-       "          [[-1.2134e-03,  7.3252e-03,  3.6696e-03],\n",
-       "           [ 5.1673e-03,  1.0149e-02,  1.1684e-02],\n",
-       "           [ 6.1404e-03,  1.4567e-02,  1.1272e-02]],\n",
-       " \n",
-       "          [[-2.3579e-03, -1.3404e-03,  6.7641e-04],\n",
-       "           [-9.3280e-03,  9.9127e-04,  6.9555e-03],\n",
-       "           [-1.2791e-02, -1.5384e-03,  1.1815e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.8942e-02,  1.4847e-02,  2.5508e-02],\n",
-       "           [ 2.7479e-02,  2.4945e-02,  3.0115e-02],\n",
-       "           [ 2.3609e-02,  2.5653e-02,  2.6047e-02]],\n",
-       " \n",
-       "          [[ 1.3801e-03, -8.3121e-03, -1.8544e-02],\n",
-       "           [-7.1930e-03, -1.2784e-02, -2.3577e-02],\n",
-       "           [-1.1942e-02,  8.0415e-04, -1.8617e-02]],\n",
-       " \n",
-       "          [[-8.7472e-03, -2.1090e-02, -1.3310e-02],\n",
-       "           [-1.3968e-02, -2.6071e-02, -3.0569e-02],\n",
-       "           [-5.6768e-03, -2.2596e-02, -2.8837e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2425, 0.2041, 0.1271, 0.0987, 0.1600, 0.0799, 0.1299, 0.1086, 0.0973,\n",
-       "         0.1165, 0.1668, 0.1033, 0.1649, 0.0783, 0.1701, 0.2070, 0.0931, 0.1074,\n",
-       "         0.1592, 0.1285, 0.1201, 0.1809, 0.1790, 0.1047, 0.1882, 0.1582, 0.2031,\n",
-       "         0.1540, 0.0725, 0.1300, 0.1389, 0.1309, 0.1791, 0.1695, 0.1696, 0.1396,\n",
-       "         0.1397, 0.1156, 0.0796, 0.1783, 0.1338, 0.1740, 0.0641, 0.1881, 0.1518,\n",
-       "         0.1581, 0.1700, 0.2009, 0.0991, 0.1006, 0.1106, 0.1191, 0.1914, 0.1137,\n",
-       "         0.1992, 0.1259, 0.1274, 0.1025, 0.1323, 0.1888, 0.1295, 0.1598, 0.1956,\n",
-       "         0.1705, 0.1794, 0.1792, 0.1551, 0.1183, 0.2087, 0.1513, 0.1411, 0.1013,\n",
-       "         0.1214, 0.1706, 0.1384, 0.1472, 0.1143, 0.0866, 0.0993, 0.6224, 0.1746,\n",
-       "         0.2519, 0.1617, 0.1101, 0.0866, 0.1109, 0.1660, 0.2303, 0.1554, 0.1341,\n",
-       "         0.1798, 0.0909, 0.1273, 0.1152, 0.0800, 0.2012, 0.1557, 0.1734, 0.1044,\n",
-       "         0.1044, 0.1544, 0.1922, 0.2254, 0.1683, 0.2431, 0.2393, 0.1519, 0.1043,\n",
-       "         0.1057, 0.1581, 0.1566, 0.1229, 0.2148, 0.1786, 0.1139, 0.1299, 0.0770,\n",
-       "         0.1825, 0.1651, 0.1290, 0.0972, 0.1237, 0.1648, 0.1320, 0.1220, 0.0677,\n",
-       "         0.2516, 0.1439, 0.1461, 0.1202, 0.1461, 0.1793, 0.2126, 0.1029, 0.1833,\n",
-       "         0.1323, 0.0910, 0.1980, 0.0838, 0.1548, 0.1418, 0.1047, 0.1868, 0.1088,\n",
-       "         0.2933, 0.1788, 0.1359, 0.1952, 0.1528, 0.1969, 0.1551, 0.2879, 0.0985,\n",
-       "         0.1366, 0.1777, 0.1877, 0.3245, 0.0755, 0.2113, 0.2261, 0.1795, 0.2340,\n",
-       "         0.3083, 0.1059, 0.2310, 0.2655, 0.2107, 0.1755, 0.1781, 0.1238, 0.1910,\n",
-       "         0.1280, 0.1350, 0.1833, 0.2064, 0.1430, 0.1783, 0.1264, 0.1419, 0.1867,\n",
-       "         0.2403, 0.2355, 0.2648, 0.1605, 0.1482, 0.1480, 0.1841, 0.2837, 0.1374,\n",
-       "         0.0925, 0.1305, 0.1630, 0.0930, 0.1719, 0.0599, 0.1294, 0.2425, 0.1179,\n",
-       "         0.1486, 0.5689, 0.2064, 0.1293, 0.2589, 0.1360, 0.0876, 0.1040, 0.1569,\n",
-       "         0.2021, 0.1082, 0.2320, 0.1428, 0.1160, 0.1533, 0.1141, 0.1855, 0.2299,\n",
-       "         0.2119, 0.1520, 0.0697, 0.0919, 0.1506, 0.2674, 0.1177, 0.2039, 0.1511,\n",
-       "         0.1201, 0.0987, 0.1383, 0.1496, 0.1545, 0.1247, 0.2629, 0.1257, 0.1192,\n",
-       "         0.2297, 0.0972, 0.1244, 0.1647, 0.1667, 0.0899, 0.2052, 0.2247, 0.1264,\n",
-       "         0.1545, 0.1584, 0.2377, 0.1329, 0.1738, 0.0855, 0.1040, 0.1593, 0.2102,\n",
-       "         0.0920, 0.2063, 0.1237, 0.0752], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.1395, -0.0801, -0.0564, -0.1342, -0.0875, -0.0485, -0.0170, -0.0381,\n",
-       "          0.0069, -0.0925, -0.2690, -0.0613, -0.1682, -0.0864, -0.1268, -0.1745,\n",
-       "         -0.0876, -0.1004, -0.1851, -0.0843, -0.0789, -0.2271, -0.0091, -0.0477,\n",
-       "          0.0131,  0.0029,  0.2076, -0.1683,  0.0459, -0.1138, -0.0977, -0.0671,\n",
-       "          0.0480, -0.1291, -0.0402, -0.1155,  0.0534,  0.0050, -0.0948, -0.2658,\n",
-       "         -0.0816, -0.2881,  0.0184,  0.0821, -0.0494, -0.0157, -0.0247, -0.0613,\n",
-       "         -0.0389, -0.0062,  0.0203, -0.0989, -0.0338, -0.0044, -0.0980,  0.0043,\n",
-       "         -0.0397,  0.0374,  0.0413, -0.0815, -0.0515, -0.1111, -0.0661, -0.1492,\n",
-       "         -0.1839,  0.1391, -0.0949, -0.0076, -0.0476, -0.0541, -0.0573, -0.0406,\n",
-       "         -0.0656, -0.1706, -0.0877, -0.1032, -0.0378, -0.0446, -0.0591,  0.0028,\n",
-       "         -0.1007, -0.1195, -0.0185, -0.0378,  0.0308, -0.0802, -0.1198, -0.1365,\n",
-       "         -0.0156, -0.0970,  0.0188, -0.0782, -0.1318, -0.1164, -0.0530,  0.0648,\n",
-       "         -0.1162, -0.1156, -0.0163, -0.0254, -0.0809, -0.1492,  0.0785, -0.0352,\n",
-       "          0.0120, -0.0636, -0.0389, -0.0639,  0.0409, -0.1045, -0.1003, -0.1926,\n",
-       "          0.0507, -0.0873, -0.0306,  0.0435, -0.0734, -0.0787, -0.1481, -0.0904,\n",
-       "         -0.0816,  0.0829, -0.1500, -0.1167, -0.1247, -0.0443, -0.1681, -0.1394,\n",
-       "         -0.2357,  0.1434, -0.1194, -0.1277, -0.1613,  0.0188,  0.0172, -0.1037,\n",
-       "         -0.0767, -0.0777,  0.0098, -0.1202,  0.0456, -0.0747, -0.1366, -0.0954,\n",
-       "         -0.2850, -0.0651, -0.1347, -0.2131, -0.0317, -0.2363, -0.0832, -0.2878,\n",
-       "         -0.0411, -0.1910,  0.0027, -0.0480, -0.0606, -0.0250,  0.0355, -0.0961,\n",
-       "         -0.1546, -0.1256, -0.2763,  0.0401, -0.1232, -0.1033, -0.1163, -0.0342,\n",
-       "         -0.0832, -0.0320, -0.0617, -0.0916, -0.0633, -0.2299, -0.1499, -0.0937,\n",
-       "         -0.0948, -0.0596,  0.0142, -0.1274, -0.2191, -0.0171, -0.2193, -0.1127,\n",
-       "         -0.0823,  0.0084, -0.0083, -0.2413, -0.0756,  0.0216, -0.0228, -0.0258,\n",
-       "         -0.0674, -0.2277, -0.0245, -0.0095, -0.1145, -0.0822, -0.1363, -0.1402,\n",
-       "         -0.1522, -0.0817,  0.0104, -0.0792, -0.0154, -0.0996,  0.0511,  0.0611,\n",
-       "         -0.1091, -0.0778, -0.0437,  0.0058, -0.2171, -0.1181, -0.1897, -0.1339,\n",
-       "          0.0363, -0.1283, -0.0114, -0.0729,  0.0353, -0.1767, -0.0419, -0.1952,\n",
-       "         -0.0468, -0.1128, -0.0406,  0.1102,  0.0256, -0.1489, -0.0784, -0.1092,\n",
-       "          0.0134,  0.0449,  0.0845, -0.0619, -0.1785, -0.2448,  0.0445, -0.1252,\n",
-       "         -0.1577, -0.0672,  0.0040,  0.0448, -0.0090, -0.0310, -0.0937, -0.1552,\n",
-       "         -0.2011, -0.0205, -0.1379, -0.2083, -0.0549, -0.1643, -0.0751,  0.0851],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 4.6370e-02,  5.6256e-04,  7.4032e-03],\n",
-       "           [ 2.1702e-02,  2.7867e-02,  7.7596e-04],\n",
-       "           [-1.0125e-02, -2.4455e-02,  4.7340e-03]],\n",
-       " \n",
-       "          [[-1.9133e-02,  2.0603e-03,  1.5773e-03],\n",
-       "           [-1.4192e-02,  1.3217e-02,  1.4653e-02],\n",
-       "           [-1.3387e-02,  1.8955e-02,  6.0380e-03]],\n",
-       " \n",
-       "          [[ 8.7729e-03, -1.6697e-03, -5.0663e-03],\n",
-       "           [ 2.1851e-02,  3.6492e-03, -1.0259e-02],\n",
-       "           [ 6.5591e-03, -9.3398e-03, -1.7276e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.7888e-03, -5.9545e-03, -4.9019e-04],\n",
-       "           [-1.8960e-03,  1.7126e-02,  1.9916e-03],\n",
-       "           [-2.3318e-02,  5.0059e-03, -1.0952e-02]],\n",
-       " \n",
-       "          [[-2.2319e-02, -1.0383e-02,  1.0482e-02],\n",
-       "           [ 2.8104e-02,  4.2813e-02, -1.2296e-02],\n",
-       "           [-1.3543e-02,  2.3008e-02, -2.1411e-02]],\n",
-       " \n",
-       "          [[-2.3627e-02, -8.3807e-03,  1.5410e-02],\n",
-       "           [-1.9845e-02, -8.6743e-03,  1.3460e-02],\n",
-       "           [-1.9803e-02, -1.1626e-02,  1.9051e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.2461e-02,  1.5637e-02, -1.6435e-03],\n",
-       "           [-3.1623e-03, -4.5702e-03, -2.1200e-02],\n",
-       "           [-1.7029e-02, -9.9041e-03, -1.1421e-02]],\n",
-       " \n",
-       "          [[-1.0539e-02,  7.9858e-04,  7.3308e-03],\n",
-       "           [-1.9827e-02,  1.9219e-03, -3.8028e-03],\n",
-       "           [-1.5197e-02,  1.7205e-03, -1.3005e-02]],\n",
-       " \n",
-       "          [[-5.9351e-03, -8.4537e-03, -1.3209e-02],\n",
-       "           [-5.6771e-03, -5.1128e-03, -1.1512e-02],\n",
-       "           [-7.6598e-03, -6.5155e-03, -1.0021e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 7.3874e-03, -5.4089e-03,  4.3885e-04],\n",
-       "           [-2.7840e-03,  7.3371e-03, -6.7416e-03],\n",
-       "           [-4.2034e-03,  7.6678e-03,  7.3041e-03]],\n",
-       " \n",
-       "          [[-1.7337e-02, -7.9295e-03, -2.7986e-02],\n",
-       "           [-3.3810e-04,  9.8717e-03, -1.0568e-02],\n",
-       "           [-2.0523e-02, -1.5734e-02, -1.1980e-02]],\n",
-       " \n",
-       "          [[-1.1486e-02, -2.7856e-02, -1.8963e-02],\n",
-       "           [-9.9338e-03, -2.2465e-02, -1.7095e-02],\n",
-       "           [-6.1334e-03, -1.2707e-02, -2.0566e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-2.5150e-03,  8.5397e-03, -5.9960e-03],\n",
-       "           [ 1.3163e-03, -2.5939e-02, -2.2034e-02],\n",
-       "           [-5.2325e-03,  5.5698e-03,  5.7646e-03]],\n",
-       " \n",
-       "          [[-1.9243e-02, -1.4463e-02, -2.3076e-02],\n",
-       "           [-2.3916e-02,  6.8570e-02,  2.1640e-02],\n",
-       "           [-2.5209e-02, -5.4117e-02, -3.0720e-02]],\n",
-       " \n",
-       "          [[-1.4042e-02,  6.6827e-03, -1.0880e-02],\n",
-       "           [ 1.0876e-02,  3.9560e-02,  4.0920e-03],\n",
-       "           [-7.5729e-04,  2.6947e-02, -1.0893e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.8144e-04,  1.1438e-03,  1.3234e-02],\n",
-       "           [ 1.6840e-02, -1.1018e-02, -1.4444e-03],\n",
-       "           [ 1.8325e-02,  3.2545e-03,  5.1347e-03]],\n",
-       " \n",
-       "          [[-1.6809e-02, -4.8663e-04, -7.5882e-03],\n",
-       "           [-3.0400e-02, -3.8977e-02, -1.5511e-02],\n",
-       "           [ 4.8797e-03,  1.8238e-02,  2.1359e-02]],\n",
-       " \n",
-       "          [[-6.6066e-03, -1.7425e-02, -6.8787e-03],\n",
-       "           [-2.0615e-02, -2.3030e-02, -6.8421e-03],\n",
-       "           [-1.3269e-02, -2.6534e-02, -5.0560e-03]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-1.4474e-02, -2.0852e-02,  9.6710e-03],\n",
-       "           [ 4.5500e-04,  3.5504e-03,  1.8124e-02],\n",
-       "           [ 4.5499e-03,  1.6035e-03,  1.7456e-02]],\n",
-       " \n",
-       "          [[-7.7204e-03, -1.3653e-02,  1.9960e-03],\n",
-       "           [-9.6841e-03, -1.8280e-02, -2.1242e-03],\n",
-       "           [-1.5821e-02,  2.7324e-03, -6.8788e-03]],\n",
-       " \n",
-       "          [[ 2.6793e-04,  1.1667e-02,  1.4397e-02],\n",
-       "           [ 7.4782e-03,  2.3565e-03,  2.2669e-02],\n",
-       "           [ 1.4568e-02, -6.0872e-04,  9.2246e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-3.5150e-03,  1.6327e-04,  3.9536e-03],\n",
-       "           [ 1.2820e-02, -6.9008e-03, -6.8466e-03],\n",
-       "           [ 1.3224e-02,  1.3377e-02,  8.6024e-03]],\n",
-       " \n",
-       "          [[ 1.4329e-03,  5.4873e-03,  2.2036e-02],\n",
-       "           [-1.4694e-02, -2.2165e-02, -2.6451e-02],\n",
-       "           [ 3.2186e-02, -1.4307e-02, -8.5657e-03]],\n",
-       " \n",
-       "          [[-3.6839e-06, -9.5868e-03,  6.4253e-03],\n",
-       "           [ 5.7980e-03, -1.6627e-02, -9.6180e-03],\n",
-       "           [ 1.4219e-02, -5.4800e-03, -1.4414e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.2142e-02,  1.5786e-02, -2.5286e-03],\n",
-       "           [-7.4835e-03,  3.2324e-03,  1.1738e-02],\n",
-       "           [ 2.3940e-02, -6.8937e-03, -2.4411e-02]],\n",
-       " \n",
-       "          [[ 1.1293e-02, -1.9195e-02,  1.2660e-02],\n",
-       "           [ 3.6045e-03, -1.6110e-02, -1.3519e-02],\n",
-       "           [-1.4058e-02, -8.9692e-03, -1.5578e-02]],\n",
-       " \n",
-       "          [[ 1.5387e-02,  2.0756e-03, -1.4415e-02],\n",
-       "           [ 1.5147e-02,  4.0560e-03, -1.9781e-03],\n",
-       "           [-5.6460e-04,  6.2269e-03,  1.2745e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.6000e-02, -1.2459e-02, -9.2975e-03],\n",
-       "           [-3.7240e-03,  1.2615e-02,  5.7884e-03],\n",
-       "           [-1.0595e-03,  8.1554e-03, -1.1370e-02]],\n",
-       " \n",
-       "          [[ 9.5620e-03, -2.0995e-02, -1.2610e-02],\n",
-       "           [ 2.9112e-03, -2.3856e-02,  4.4539e-03],\n",
-       "           [-5.2280e-03, -1.5701e-02,  7.1438e-03]],\n",
-       " \n",
-       "          [[ 9.2711e-03,  8.1973e-03, -7.4169e-04],\n",
-       "           [-4.3421e-03,  2.3019e-03, -1.1614e-02],\n",
-       "           [-1.8474e-02, -1.0923e-02, -5.1620e-04]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 8.2245e-03, -2.9684e-02, -1.0133e-02],\n",
-       "           [-2.9106e-02, -3.8408e-02,  1.8098e-03],\n",
-       "           [-1.7324e-02, -2.4908e-02, -1.6642e-02]],\n",
-       " \n",
-       "          [[-1.4682e-02,  1.2883e-02,  6.2379e-03],\n",
-       "           [ 9.9670e-03, -2.1070e-03,  2.0871e-02],\n",
-       "           [-1.3605e-02, -8.7752e-03,  5.2294e-03]],\n",
-       " \n",
-       "          [[-5.2459e-03,  8.8940e-04,  1.5815e-02],\n",
-       "           [ 9.6540e-05, -3.5013e-03, -3.1062e-03],\n",
-       "           [ 1.1937e-02, -1.3105e-03, -6.9573e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 9.0118e-04, -1.2930e-02, -8.7528e-03],\n",
-       "           [ 1.6854e-02,  7.7208e-04, -1.3369e-02],\n",
-       "           [ 5.8840e-03,  3.2350e-03, -2.2324e-02]],\n",
-       " \n",
-       "          [[-7.7531e-03, -1.1344e-02,  2.3870e-04],\n",
-       "           [-2.6686e-02, -1.2072e-02,  4.5224e-03],\n",
-       "           [-1.9576e-02, -2.2625e-02, -2.4721e-03]],\n",
-       " \n",
-       "          [[ 8.4222e-03, -1.3148e-03, -6.3456e-03],\n",
-       "           [ 2.4230e-03, -2.7047e-03, -1.6911e-02],\n",
-       "           [ 1.4639e-02,  1.4781e-02, -3.6172e-03]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2248, 0.2273, 0.2581, 0.1921, 0.2066, 0.2356, 0.2081, 0.2192, 0.2422,\n",
-       "         0.2359, 0.2129, 0.2067, 0.2090, 0.2100, 0.2158, 0.2248, 0.2625, 0.2068,\n",
-       "         0.1973, 0.2191, 0.1833, 0.2043, 0.1897, 0.2106, 0.2177, 0.2451, 0.2503,\n",
-       "         0.2708, 0.1847, 0.2058, 0.1958, 0.2463, 0.2253, 0.1922, 0.2334, 0.2244,\n",
-       "         0.2115, 0.2998, 0.2768, 0.2718, 0.1245, 0.1607, 0.2154, 0.2333, 0.2140,\n",
-       "         0.2183, 0.2166, 0.2382, 0.1769, 0.1727, 0.2233, 0.1769, 0.2635, 0.2236,\n",
-       "         0.2273, 0.1995, 0.2767, 0.2005, 0.2467, 0.2363, 0.2303, 0.2075, 0.2304,\n",
-       "         0.2046, 0.2543, 0.2573, 0.1826, 0.2169, 0.2593, 0.1884, 0.1850, 0.1809,\n",
-       "         0.1843, 0.2526, 0.1936, 0.1918, 0.2821, 0.2200, 0.2082, 0.1880, 0.2788,\n",
-       "         0.2010, 0.2261, 0.2725, 0.1921, 0.2571, 0.1396, 0.1898, 0.1772, 0.1972,\n",
-       "         0.2600, 0.1904, 0.2180, 0.2640, 0.2122, 0.1959, 0.2405, 0.1961, 0.1887,\n",
-       "         0.2254, 0.1838, 0.1698, 0.2877, 0.2232, 0.2542, 0.2115, 0.2833, 0.2444,\n",
-       "         0.2103, 0.2204, 0.2465, 0.1928, 0.2413, 0.2459, 0.2222, 0.2097, 0.2263,\n",
-       "         0.1738, 0.1595, 0.2047, 0.2168, 0.2294, 0.2260, 0.2232, 0.2084, 0.2194,\n",
-       "         0.2099, 0.1996, 0.2184, 0.2297, 0.2470, 0.2306, 0.2097, 0.2184, 0.1940,\n",
-       "         0.2078, 0.2587, 0.2136, 0.1994, 0.2312, 0.1741, 0.2092, 0.2339, 0.2382,\n",
-       "         0.2584, 0.2382, 0.2351, 0.2404, 0.2379, 0.2840, 0.1530, 0.1645, 0.2547,\n",
-       "         0.1996, 0.2318, 0.2508, 0.1944, 0.2085, 0.1977, 0.2621, 0.2392, 0.2414,\n",
-       "         0.2424, 0.2414, 0.1813, 0.2606, 0.2215, 0.1869, 0.1929, 0.2007, 0.2293,\n",
-       "         0.2248, 0.2140, 0.2783, 0.2261, 0.2148, 0.3297, 0.2150, 0.1682, 0.2175,\n",
-       "         0.2298, 0.2164, 0.1827, 0.2229, 0.1759, 0.2217, 0.2294, 0.2174, 0.2059,\n",
-       "         0.2066, 0.2379, 0.2658, 0.2107, 0.2438, 0.2025, 0.2074, 0.2052, 0.2188,\n",
-       "         0.2277, 0.1857, 0.2324, 0.2263, 0.1935, 0.2091, 0.2096, 0.2284, 0.2312,\n",
-       "         0.1861, 0.2230, 0.2200, 0.2356, 0.2481, 0.2311, 0.2057, 0.2035, 0.1989,\n",
-       "         0.2472, 0.2413, 0.1551, 0.2172, 0.2321, 0.2308, 0.1799, 0.1933, 0.1906,\n",
-       "         0.2324, 0.2923, 0.2633, 0.2244, 0.1840, 0.2202, 0.2899, 0.1988, 0.2133,\n",
-       "         0.2424, 0.2136, 0.2108, 0.1789, 0.2236, 0.2450, 0.1909, 0.1981, 0.2219,\n",
-       "         0.2569, 0.1633, 0.1865, 0.2301, 0.2108, 0.2078, 0.2149, 0.1961, 0.1937,\n",
-       "         0.2002, 0.2143, 0.2168, 0.1907], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.2593, -0.2566, -0.3305, -0.1655, -0.1934, -0.2323, -0.2380, -0.1924,\n",
-       "         -0.2664, -0.2240, -0.2454, -0.2370, -0.1941, -0.2166, -0.1534, -0.1374,\n",
-       "         -0.3869, -0.1703, -0.1196, -0.2741, -0.1118, -0.2842, -0.1547, -0.2202,\n",
-       "         -0.2479, -0.1985, -0.2800, -0.3516, -0.1846, -0.2356, -0.1671, -0.2839,\n",
-       "         -0.2222, -0.0891, -0.2063, -0.2775, -0.1367, -0.2919, -0.3855, -0.2748,\n",
-       "          0.0269, -0.1712, -0.1482, -0.1931, -0.1420, -0.2336, -0.1890, -0.2517,\n",
-       "         -0.0847, -0.0907, -0.2158, -0.0847, -0.2128, -0.2058, -0.1716, -0.1756,\n",
-       "         -0.2719, -0.1300, -0.2242, -0.2493, -0.1810, -0.1394, -0.2959, -0.2159,\n",
-       "         -0.1747, -0.3479, -0.1671, -0.2345, -0.2819, -0.1889, -0.1601, -0.1519,\n",
-       "         -0.2012, -0.2554, -0.1682, -0.1838, -0.3191, -0.2360, -0.1688, -0.1347,\n",
-       "         -0.3173, -0.1500, -0.1963, -0.3256, -0.1764, -0.2490, -0.0226, -0.1456,\n",
-       "         -0.0849, -0.1750, -0.2342, -0.1374, -0.1792, -0.3394, -0.1926, -0.1674,\n",
-       "         -0.2801, -0.2413, -0.2332, -0.2238, -0.1344, -0.1339, -0.3203, -0.2263,\n",
-       "         -0.2029, -0.1763, -0.3308, -0.3212, -0.1593, -0.3396, -0.3608, -0.1552,\n",
-       "         -0.3736, -0.2906, -0.2178, -0.1857, -0.2014, -0.1921, -0.2008, -0.1417,\n",
-       "         -0.1544, -0.2564, -0.2886, -0.1997, -0.1895, -0.1535, -0.1806, -0.1918,\n",
-       "         -0.2046, -0.2078, -0.2692, -0.2461, -0.1808, -0.2089, -0.1199, -0.0932,\n",
-       "         -0.3684, -0.2450, -0.1977, -0.2755, -0.1077, -0.1622, -0.2201, -0.2608,\n",
-       "         -0.2477, -0.2827, -0.2402, -0.2738, -0.2946, -0.3076,  0.0019, -0.1663,\n",
-       "         -0.3339, -0.2037, -0.2569, -0.2339, -0.1015, -0.2390, -0.2784, -0.2987,\n",
-       "         -0.2317, -0.2546, -0.2781, -0.2480, -0.1814, -0.3326, -0.1912, -0.1238,\n",
-       "         -0.1900, -0.1905, -0.2807, -0.2524, -0.1880, -0.3377, -0.1957, -0.1569,\n",
-       "         -0.5650, -0.2143, -0.1325, -0.2323, -0.3275, -0.2287, -0.1981, -0.1705,\n",
-       "         -0.2746, -0.2240, -0.2722, -0.1934, -0.1942, -0.1491, -0.2562, -0.2897,\n",
-       "         -0.1503, -0.2874, -0.1573, -0.2378, -0.1655, -0.2520, -0.2348, -0.0822,\n",
-       "         -0.2127, -0.3074, -0.2076, -0.2563, -0.1999, -0.1982, -0.2568, -0.1199,\n",
-       "         -0.1599, -0.2166, -0.2564, -0.2481, -0.3291, -0.1318, -0.1811, -0.1869,\n",
-       "         -0.2285, -0.2589, -0.0381, -0.2317, -0.2568, -0.1943, -0.1472, -0.1981,\n",
-       "         -0.1922, -0.3463, -0.3734, -0.3697, -0.2494, -0.1253, -0.2082, -0.4252,\n",
-       "         -0.2127, -0.1692, -0.3904, -0.2622, -0.2480, -0.1438, -0.1917, -0.4144,\n",
-       "         -0.1721, -0.2124, -0.2238, -0.2123, -0.1737, -0.2015, -0.2442, -0.1025,\n",
-       "         -0.1720, -0.2940, -0.1733, -0.1820, -0.2112, -0.1639, -0.2852, -0.1985],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-1.3173e-02, -3.0688e-02,  1.9353e-03],\n",
-       "           [ 2.3906e-02, -2.0767e-02, -3.9549e-02],\n",
-       "           [ 1.5298e-02,  2.8872e-04, -1.6832e-02]],\n",
-       " \n",
-       "          [[-1.4412e-02, -1.6179e-02, -7.1448e-04],\n",
-       "           [-7.7069e-03, -1.5456e-02, -1.6121e-02],\n",
-       "           [ 1.8065e-02,  3.3236e-03,  1.0312e-03]],\n",
-       " \n",
-       "          [[-7.6273e-03, -1.0432e-02, -1.3261e-02],\n",
-       "           [ 3.1007e-03,  4.2611e-03,  2.4167e-03],\n",
-       "           [ 1.0849e-02, -1.9346e-02,  8.0113e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.3553e-02, -1.7156e-02, -2.6124e-02],\n",
-       "           [-1.1392e-02, -2.5640e-02, -1.1132e-02],\n",
-       "           [-5.0302e-03, -1.5564e-02, -2.1543e-03]],\n",
-       " \n",
-       "          [[ 9.0471e-04, -1.0772e-02,  4.9236e-03],\n",
-       "           [-1.3463e-02, -2.5419e-02, -1.0858e-02],\n",
-       "           [ 4.8300e-03, -1.2313e-02, -2.2698e-02]],\n",
-       " \n",
-       "          [[ 6.0922e-03, -1.1981e-02,  3.8873e-03],\n",
-       "           [-1.4200e-02,  1.7599e-03, -8.4445e-03],\n",
-       "           [ 2.8755e-02,  3.2615e-02, -2.5738e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-9.5593e-03, -2.3259e-02,  3.0557e-03],\n",
-       "           [-3.4763e-03,  8.0465e-03,  4.3056e-02],\n",
-       "           [ 5.1915e-03,  3.5145e-03,  5.3361e-03]],\n",
-       " \n",
-       "          [[-1.1128e-02, -5.9102e-03,  7.7002e-03],\n",
-       "           [-3.2827e-02, -2.9058e-02, -1.3808e-02],\n",
-       "           [-2.2832e-02, -2.5739e-02, -2.0009e-02]],\n",
-       " \n",
-       "          [[ 2.9134e-02,  1.7871e-02, -4.2709e-03],\n",
-       "           [ 2.4726e-02,  5.2472e-03,  1.3521e-02],\n",
-       "           [ 1.8482e-02,  3.0739e-02,  2.0953e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.1724e-03,  5.8616e-03, -1.1028e-02],\n",
-       "           [-5.9195e-04, -1.1071e-02,  9.9208e-03],\n",
-       "           [ 1.1104e-02, -1.0325e-02,  3.4384e-04]],\n",
-       " \n",
-       "          [[-3.3301e-02, -3.4656e-02, -1.9131e-02],\n",
-       "           [ 1.6308e-02, -6.7419e-03, -1.5647e-02],\n",
-       "           [ 1.4805e-02, -3.6313e-03, -1.0887e-02]],\n",
-       " \n",
-       "          [[ 1.0353e-02, -5.6899e-03, -4.3294e-03],\n",
-       "           [ 2.2922e-03, -8.9911e-03,  5.2422e-03],\n",
-       "           [-2.7371e-02, -1.7996e-02, -8.9814e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-3.0436e-03, -1.4966e-02, -2.2756e-02],\n",
-       "           [-1.5525e-02, -1.4894e-02, -4.2565e-03],\n",
-       "           [ 1.9280e-03,  4.9518e-03,  1.8052e-02]],\n",
-       " \n",
-       "          [[-1.8257e-02, -1.1268e-02, -2.8320e-03],\n",
-       "           [-7.1109e-05,  4.9135e-03,  1.0005e-02],\n",
-       "           [ 5.5669e-03, -4.9948e-03,  5.5305e-03]],\n",
-       " \n",
-       "          [[ 2.0791e-03,  1.1920e-02,  1.4088e-02],\n",
-       "           [-3.1730e-03,  2.4283e-03,  1.4026e-02],\n",
-       "           [ 1.5001e-02,  6.9429e-05,  1.4456e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.6758e-03,  3.9626e-03, -1.4841e-03],\n",
-       "           [ 5.6928e-03, -8.3725e-04, -1.6499e-02],\n",
-       "           [ 1.3384e-02,  2.7561e-03, -9.9029e-03]],\n",
-       " \n",
-       "          [[ 1.1106e-02,  7.8462e-03,  3.0965e-03],\n",
-       "           [-1.5397e-04,  1.0497e-03, -1.0333e-02],\n",
-       "           [-3.5632e-03, -1.3445e-02, -2.3245e-02]],\n",
-       " \n",
-       "          [[ 2.3631e-03, -5.6996e-03,  4.7789e-03],\n",
-       "           [ 1.6247e-03, -5.3197e-03,  2.2292e-03],\n",
-       "           [-1.1096e-02, -1.4389e-02,  7.6514e-03]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-1.3582e-03, -1.0963e-02, -2.8291e-02],\n",
-       "           [ 2.1695e-03,  4.5784e-03,  1.6181e-03],\n",
-       "           [ 5.9154e-03, -7.7224e-03,  2.2288e-03]],\n",
-       " \n",
-       "          [[ 1.3355e-03, -1.3968e-02, -2.0281e-02],\n",
-       "           [-9.4218e-03, -1.2451e-02, -1.9137e-02],\n",
-       "           [-6.4536e-03, -6.4755e-03, -5.5464e-03]],\n",
-       " \n",
-       "          [[ 6.4362e-04, -2.0385e-02, -1.3892e-02],\n",
-       "           [ 8.2709e-03, -1.1668e-03, -1.8568e-02],\n",
-       "           [ 1.6538e-02,  1.1921e-03,  7.8253e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.1331e-02, -1.5788e-02, -4.3070e-03],\n",
-       "           [-6.6293e-03, -1.1943e-02, -1.4448e-02],\n",
-       "           [-1.0737e-02, -2.3444e-03, -1.0166e-02]],\n",
-       " \n",
-       "          [[ 9.4696e-03,  6.0534e-03,  1.6166e-02],\n",
-       "           [ 2.5334e-02,  4.7381e-03,  2.2657e-02],\n",
-       "           [ 1.8172e-02,  2.0458e-02,  1.5880e-02]],\n",
-       " \n",
-       "          [[ 1.4649e-03, -7.8944e-03,  1.1223e-02],\n",
-       "           [ 7.9414e-05, -1.0588e-02,  1.1868e-02],\n",
-       "           [ 4.6165e-03,  5.4022e-03,  1.2166e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-4.2605e-03, -3.4046e-04, -1.1347e-02],\n",
-       "           [ 7.8405e-03,  3.1981e-02, -3.0250e-03],\n",
-       "           [-2.6192e-03, -6.6952e-04, -3.1039e-03]],\n",
-       " \n",
-       "          [[-1.7597e-02, -5.1213e-02, -9.5706e-03],\n",
-       "           [-4.3486e-02, -7.9187e-02, -2.8701e-02],\n",
-       "           [-7.4132e-04, -3.0772e-02,  6.7875e-03]],\n",
-       " \n",
-       "          [[ 2.0307e-03, -1.9364e-02, -3.8252e-03],\n",
-       "           [-1.2643e-02, -3.3784e-02, -1.5509e-02],\n",
-       "           [-1.0624e-02, -1.6939e-02, -1.2053e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 3.5399e-03,  2.5195e-02,  1.1569e-02],\n",
-       "           [ 1.0142e-02,  3.2678e-02,  1.3360e-02],\n",
-       "           [ 9.6261e-03,  9.2737e-03,  1.3063e-04]],\n",
-       " \n",
-       "          [[ 1.3794e-02, -9.2433e-04, -7.4184e-03],\n",
-       "           [ 2.6872e-02,  2.4649e-03, -3.7844e-03],\n",
-       "           [ 4.9833e-02,  2.1156e-02,  6.5408e-05]],\n",
-       " \n",
-       "          [[-6.1022e-03,  4.8877e-03, -1.5924e-02],\n",
-       "           [-3.5923e-03, -1.3191e-03, -3.2014e-02],\n",
-       "           [ 8.2219e-03, -3.6217e-03, -1.5103e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.7643e-02, -2.9575e-02, -1.7077e-02],\n",
-       "           [-1.5184e-02, -2.5616e-02, -2.0600e-02],\n",
-       "           [-6.9351e-04, -1.2414e-02, -1.2472e-02]],\n",
-       " \n",
-       "          [[ 9.5399e-04, -9.7229e-03, -4.2267e-03],\n",
-       "           [-1.2264e-02, -2.7419e-02, -2.8418e-02],\n",
-       "           [-1.2285e-02, -3.0184e-02, -2.8565e-02]],\n",
-       " \n",
-       "          [[-9.6834e-04,  1.7716e-03,  1.8684e-03],\n",
-       "           [ 1.8813e-02, -2.2885e-03,  3.0467e-02],\n",
-       "           [ 5.4492e-03,  1.1453e-02,  1.9083e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.4035e-02,  1.0879e-02,  3.8342e-03],\n",
-       "           [ 5.7757e-03,  7.0556e-03,  5.6085e-03],\n",
-       "           [ 4.2769e-03,  8.0404e-03,  3.0379e-04]],\n",
-       " \n",
-       "          [[ 9.1897e-03,  4.8496e-03, -7.6210e-03],\n",
-       "           [ 3.6545e-04, -1.0781e-02, -1.1173e-02],\n",
-       "           [ 4.0353e-03, -1.0573e-02, -2.2953e-02]],\n",
-       " \n",
-       "          [[-2.1005e-02, -2.7807e-02, -9.8587e-03],\n",
-       "           [-2.4677e-02, -3.0678e-02, -4.1122e-03],\n",
-       "           [-2.5526e-02, -1.4259e-02,  3.7883e-04]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.3108, 0.1754, 0.1488, 0.0638, 0.2715, 0.0763, 0.1964, 0.0543, 0.1039,\n",
-       "         0.0650, 0.1922, 0.0734, 0.1853, 0.0448, 0.1183, 0.0991, 0.1476, 0.0669,\n",
-       "         0.0479, 0.1073, 0.0929, 0.1551, 0.2616, 0.0830, 0.1603, 0.1094, 0.1899,\n",
-       "         0.1224, 0.0317, 0.0809, 0.1885, 0.1329, 0.2178, 0.0866, 0.1893, 0.0936,\n",
-       "         0.1446, 0.1548, 0.0593, 0.0721, 0.0679, 0.2954, 0.0448, 0.1402, 0.1567,\n",
-       "         0.1767, 0.1100, 0.2048, 0.0273, 0.0772, 0.1106, 0.1147, 0.2414, 0.1072,\n",
-       "         0.2136, 0.0388, 0.1511, 0.1135, 0.2486, 0.2143, 0.2507, 0.1235, 0.2981,\n",
-       "         0.1083, 0.1721, 0.2435, 0.1621, 0.0937, 0.1939, 0.2038, 0.2001, 0.0815,\n",
-       "         0.1079, 0.1035, 0.1172, 0.2051, 0.1625, 0.0941, 0.1092, 0.2884, 0.2069,\n",
-       "         0.3112, 0.1915, 0.1211, 0.1424, 0.1012, 0.0932, 0.2896, 0.1763, 0.1425,\n",
-       "         0.1869, 0.0708, 0.0527, 0.0923, 0.1080, 0.1914, 0.0953, 0.2197, 0.0855,\n",
-       "         0.0986, 0.1195, 0.1819, 0.2459, 0.1569, 0.2392, 0.3192, 0.1489, 0.1144,\n",
-       "         0.1522, 0.1633, 0.1869, 0.0609, 0.2051, 0.1989, 0.1139, 0.1415, 0.0647,\n",
-       "         0.1060, 0.1062, 0.1382, 0.0512, 0.1944, 0.0639, 0.2050, 0.0901, 0.0595,\n",
-       "         0.3877, 0.0949, 0.0937, 0.0867, 0.1409, 0.0902, 0.2447, 0.1437, 0.2335,\n",
-       "         0.1922, 0.0817, 0.2392, 0.0732, 0.1255, 0.1622, 0.1491, 0.1861, 0.0434,\n",
-       "         0.3223, 0.1893, 0.1405, 0.0687, 0.1723, 0.1731, 0.1660, 0.3443, 0.0949,\n",
-       "         0.0483, 0.2315, 0.2388, 0.2174, 0.0919, 0.2217, 0.2367, 0.2322, 0.3047,\n",
-       "         0.3491, 0.1084, 0.2102, 0.3950, 0.2547, 0.1410, 0.2130, 0.2513, 0.2029,\n",
-       "         0.1483, 0.1768, 0.1783, 0.3169, 0.1701, 0.1896, 0.0913, 0.1431, 0.1435,\n",
-       "         0.2362, 0.3009, 0.2679, 0.2011, 0.1229, 0.1549, 0.2060, 0.2957, 0.1330,\n",
-       "         0.1009, 0.0505, 0.0934, 0.0681, 0.0894, 0.0747, 0.2043, 0.3920, 0.1310,\n",
-       "         0.1784, 0.2740, 0.2658, 0.1147, 0.1700, 0.1169, 0.0551, 0.0603, 0.1601,\n",
-       "         0.2815, 0.1811, 0.2590, 0.1144, 0.1175, 0.1129, 0.0743, 0.2600, 0.3047,\n",
-       "         0.2956, 0.1207, 0.1175, 0.0687, 0.1881, 0.2534, 0.0878, 0.3558, 0.2145,\n",
-       "         0.1217, 0.1171, 0.1625, 0.1553, 0.1899, 0.1197, 0.2431, 0.0788, 0.1255,\n",
-       "         0.2591, 0.0549, 0.0640, 0.0664, 0.1745, 0.0356, 0.1716, 0.1901, 0.1144,\n",
-       "         0.1873, 0.2846, 0.2424, 0.1455, 0.1755, 0.0406, 0.1033, 0.1479, 0.3258,\n",
-       "         0.0574, 0.1016, 0.1043, 0.0828], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.1563, -0.1540, -0.0165,  0.0086, -0.1343, -0.0536, -0.2012, -0.0689,\n",
-       "         -0.0214, -0.0444, -0.1474, -0.0179, -0.0862, -0.0284, -0.0674, -0.0618,\n",
-       "         -0.1014, -0.0146, -0.0353, -0.0536, -0.0194, -0.1052, -0.0205, -0.0338,\n",
-       "         -0.0359, -0.0818,  0.0855, -0.0552, -0.0133, -0.0519, -0.0883, -0.0650,\n",
-       "         -0.1505, -0.0355, -0.0708, -0.1088,  0.0414, -0.0635,  0.0416, -0.0909,\n",
-       "         -0.0155, -0.0305, -0.0088,  0.0123, -0.0427,  0.0182, -0.0647, -0.1396,\n",
-       "          0.0163, -0.0355,  0.0326, -0.0675, -0.0958, -0.0317, -0.0945, -0.0196,\n",
-       "         -0.0317, -0.0844, -0.0497, -0.1147,  0.0790, -0.0617, -0.1331, -0.0968,\n",
-       "         -0.1753,  0.0628, -0.0961, -0.0292, -0.0908, -0.1064, -0.1606, -0.0321,\n",
-       "          0.0258, -0.0638, -0.0364, -0.1191, -0.0610, -0.0663,  0.0187, -0.2643,\n",
-       "         -0.0531, -0.2344, -0.1269, -0.0904,  0.1076, -0.0245,  0.0087, -0.1516,\n",
-       "         -0.0126, -0.0648, -0.0924, -0.0414, -0.0111, -0.0119, -0.0540, -0.0670,\n",
-       "         -0.0328, -0.0940, -0.0054, -0.0745, -0.0597, -0.0621, -0.0695, -0.0634,\n",
-       "         -0.1644, -0.1019, -0.0017, -0.0485, -0.0459, -0.0972, -0.1481, -0.0054,\n",
-       "         -0.0334, -0.1252, -0.0067, -0.0428,  0.0060, -0.0377, -0.0732, -0.0381,\n",
-       "         -0.0088,  0.0113,  0.0102, -0.0955, -0.0444,  0.0013, -0.1768, -0.0810,\n",
-       "         -0.0690,  0.0778, -0.1177, -0.0245, -0.1192, -0.0494, -0.1243, -0.0854,\n",
-       "         -0.0505, -0.1866, -0.0519, -0.0394, -0.0414, -0.1058, -0.0603, -0.0007,\n",
-       "         -0.0394,  0.0708,  0.0856, -0.0392, -0.1035, -0.0082, -0.0650, -0.2681,\n",
-       "         -0.0431, -0.0302, -0.1441, -0.0993, -0.1215, -0.0522, -0.0931, -0.1988,\n",
-       "         -0.1817, -0.1404, -0.2839, -0.0589,  0.0967, -0.0256, -0.1464, -0.0399,\n",
-       "         -0.0368, -0.0050, -0.0911, -0.0596, -0.0036, -0.1557, -0.1047,  0.0554,\n",
-       "         -0.1471, -0.0176, -0.0160, -0.0673,  0.0205,  0.0100, -0.2260, -0.1166,\n",
-       "         -0.1082, -0.1009, -0.0836, -0.3246, -0.0741, -0.0445, -0.0455,  0.0063,\n",
-       "         -0.0311, -0.0481, -0.0032, -0.1013, -0.2277,  0.0338, -0.1157, -0.2231,\n",
-       "         -0.1921, -0.0566, -0.0813, -0.0477, -0.0440, -0.0296, -0.0202,  0.0339,\n",
-       "          0.0951, -0.1213, -0.0428, -0.0126, -0.0985,  0.0590, -0.3005, -0.1852,\n",
-       "         -0.0396, -0.0666, -0.0447, -0.0221, -0.0190, -0.1209, -0.0367, -0.1101,\n",
-       "          0.0034, -0.1190, -0.0426, -0.0011, -0.0460, -0.0115, -0.0800, -0.0236,\n",
-       "          0.0184, -0.0167, -0.1193,  0.0248,  0.0828, -0.0646, -0.0469,  0.0019,\n",
-       "         -0.0704, -0.1604, -0.0210, -0.0585, -0.2201, -0.0679, -0.0752, -0.0589,\n",
-       "          0.0545, -0.0683, -0.0080, -0.1055, -0.0147, -0.0207, -0.0808,  0.0405],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-4.1190e-02, -5.7649e-03,  3.1938e-02],\n",
-       "           [-9.5346e-03, -3.0917e-03,  3.2099e-02],\n",
-       "           [ 1.7147e-02,  1.3704e-02, -1.3396e-03]],\n",
-       " \n",
-       "          [[ 2.8125e-02,  2.1814e-02,  9.1667e-03],\n",
-       "           [ 1.4301e-02,  3.0258e-02,  9.2201e-03],\n",
-       "           [ 6.7626e-03, -4.8516e-03, -1.5761e-02]],\n",
-       " \n",
-       "          [[ 1.7542e-02,  1.9403e-02,  1.4117e-02],\n",
-       "           [ 1.6638e-05,  6.3412e-03,  5.1177e-03],\n",
-       "           [-1.8701e-02, -6.9207e-03, -1.1607e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 8.2205e-03,  2.2129e-02, -7.4113e-04],\n",
-       "           [ 9.1494e-03, -1.2546e-02,  1.1613e-02],\n",
-       "           [ 2.1905e-02, -5.8960e-03,  7.9101e-03]],\n",
-       " \n",
-       "          [[ 1.4380e-02,  1.1169e-02,  1.0807e-03],\n",
-       "           [-1.3844e-02, -1.9678e-02,  1.7014e-02],\n",
-       "           [ 2.2529e-03, -7.1527e-03,  2.3414e-02]],\n",
-       " \n",
-       "          [[ 1.1161e-02, -1.5278e-02, -2.0407e-02],\n",
-       "           [-2.1299e-03, -2.1525e-02, -1.9509e-02],\n",
-       "           [-8.9369e-03, -1.2678e-02, -1.0611e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-6.6363e-03, -2.2789e-02, -5.8837e-03],\n",
-       "           [ 3.4187e-02,  1.9290e-03, -1.4664e-02],\n",
-       "           [ 1.0739e-02,  3.2266e-02,  2.4941e-02]],\n",
-       " \n",
-       "          [[-1.5910e-02, -1.0367e-02, -1.1980e-02],\n",
-       "           [-1.9279e-02, -2.4827e-02, -8.3954e-03],\n",
-       "           [-2.0348e-02, -1.8470e-02, -1.1543e-02]],\n",
-       " \n",
-       "          [[ 1.6426e-02,  3.0828e-02,  4.1144e-02],\n",
-       "           [ 4.9883e-03,  4.8438e-03,  1.1484e-02],\n",
-       "           [ 5.4824e-03, -9.2771e-03,  3.6135e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-5.9588e-04, -1.0022e-02, -1.2531e-03],\n",
-       "           [ 5.9828e-03, -7.3835e-03,  1.6288e-02],\n",
-       "           [ 1.2974e-02, -1.3132e-02,  3.7154e-03]],\n",
-       " \n",
-       "          [[-1.1445e-02,  1.5335e-02,  5.3890e-03],\n",
-       "           [-1.0985e-03,  1.9071e-04,  1.1295e-02],\n",
-       "           [ 1.3436e-03, -1.1085e-02,  9.3950e-03]],\n",
-       " \n",
-       "          [[ 1.5600e-03,  1.6189e-02,  6.4427e-03],\n",
-       "           [-6.5096e-03,  1.0693e-02, -9.8305e-03],\n",
-       "           [-1.7058e-02, -8.6583e-03, -2.3226e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.2434e-02,  1.2241e-03, -4.8340e-03],\n",
-       "           [ 1.0255e-02,  6.3828e-04, -4.5285e-03],\n",
-       "           [-1.1050e-02, -9.2360e-03,  1.6016e-03]],\n",
-       " \n",
-       "          [[ 7.6409e-03, -8.2412e-03, -1.6597e-02],\n",
-       "           [ 1.5888e-02, -6.4941e-03, -2.3207e-02],\n",
-       "           [ 3.8955e-03, -8.5275e-03, -1.0762e-02]],\n",
-       " \n",
-       "          [[-2.2963e-03,  1.2378e-02,  1.6760e-04],\n",
-       "           [-8.3214e-03,  8.6798e-03, -1.4447e-04],\n",
-       "           [-1.0806e-02,  1.7924e-04, -4.2665e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.7336e-03,  4.6905e-04,  4.9637e-03],\n",
-       "           [ 5.6722e-03, -2.0630e-03, -5.7050e-03],\n",
-       "           [ 9.9568e-03, -3.9040e-03, -2.0470e-02]],\n",
-       " \n",
-       "          [[-9.6803e-03, -2.4351e-02, -5.7609e-03],\n",
-       "           [-1.4628e-02, -1.4350e-02,  8.1144e-03],\n",
-       "           [-1.2008e-02,  9.1849e-03,  4.4149e-03]],\n",
-       " \n",
-       "          [[-2.0225e-02, -1.3689e-02, -2.3851e-02],\n",
-       "           [-2.2294e-02, -1.0385e-02, -1.1026e-02],\n",
-       "           [-2.1532e-02, -7.2313e-03, -5.6968e-03]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-1.4532e-02, -4.9330e-02, -3.8154e-02],\n",
-       "           [ 3.7983e-02,  3.7639e-02, -3.9978e-02],\n",
-       "           [ 2.0656e-02,  7.2771e-02,  5.0211e-02]],\n",
-       " \n",
-       "          [[ 1.0308e-02, -2.1022e-03,  1.2604e-03],\n",
-       "           [-4.1194e-04, -1.6920e-02, -1.3341e-02],\n",
-       "           [-5.6290e-03, -5.1357e-03, -1.0650e-02]],\n",
-       " \n",
-       "          [[ 3.2567e-02,  5.1097e-02,  6.1861e-02],\n",
-       "           [ 2.4667e-03,  2.2743e-02,  4.5047e-02],\n",
-       "           [-2.3327e-02, -1.0478e-02,  2.8729e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.8240e-02, -6.4529e-03, -2.4623e-02],\n",
-       "           [ 5.1531e-03,  4.1069e-03, -1.2301e-03],\n",
-       "           [ 6.1034e-03,  8.7667e-03, -9.8778e-03]],\n",
-       " \n",
-       "          [[-8.1502e-04,  1.9997e-04, -2.9231e-04],\n",
-       "           [ 7.0016e-03, -3.6142e-03, -9.7795e-03],\n",
-       "           [ 2.4394e-02,  5.2492e-02, -8.5456e-03]],\n",
-       " \n",
-       "          [[ 1.1052e-02, -1.2259e-02, -1.8370e-02],\n",
-       "           [ 1.9792e-02,  4.3409e-03,  3.4139e-04],\n",
-       "           [ 1.1640e-02,  1.5248e-02,  1.1279e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 3.8920e-03, -1.1707e-02,  3.0948e-03],\n",
-       "           [-1.4054e-02, -2.0145e-02,  3.2802e-03],\n",
-       "           [ 2.3631e-02,  7.7877e-03, -2.9382e-03]],\n",
-       " \n",
-       "          [[ 1.3299e-02,  6.1512e-03,  7.1296e-03],\n",
-       "           [-1.0920e-02, -9.4875e-03,  1.2243e-03],\n",
-       "           [ 3.6697e-03,  8.5293e-03,  2.7406e-03]],\n",
-       " \n",
-       "          [[ 7.8390e-03,  7.7010e-03,  7.6813e-04],\n",
-       "           [-2.3091e-02, -1.1519e-02, -9.6764e-03],\n",
-       "           [-2.6256e-02, -2.8006e-03,  8.2084e-04]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.7048e-02,  2.4427e-04, -1.3277e-02],\n",
-       "           [-1.7098e-02,  1.3229e-02, -8.6379e-03],\n",
-       "           [ 5.2723e-03,  1.0553e-02,  1.2252e-02]],\n",
-       " \n",
-       "          [[-1.5645e-02,  7.3183e-03,  4.6613e-03],\n",
-       "           [ 1.7057e-02,  1.7915e-02,  7.1986e-03],\n",
-       "           [ 1.0995e-02,  1.0139e-02,  1.6008e-02]],\n",
-       " \n",
-       "          [[-8.7951e-03, -4.8768e-04, -1.9019e-03],\n",
-       "           [-8.2163e-03,  8.8732e-03,  2.0239e-03],\n",
-       "           [-1.1298e-02, -4.1315e-03, -3.0040e-04]]],\n",
-       " \n",
-       " \n",
-       "         [[[-2.6540e-02, -4.7237e-02, -5.2408e-02],\n",
-       "           [-2.3632e-03, -3.8126e-03, -2.8519e-02],\n",
-       "           [ 1.7096e-02,  2.3917e-03, -4.0268e-03]],\n",
-       " \n",
-       "          [[-9.3307e-04,  9.1628e-03,  1.6886e-02],\n",
-       "           [-7.2631e-03,  3.4543e-02, -2.9300e-03],\n",
-       "           [-2.0639e-02, -2.4870e-03, -2.1146e-02]],\n",
-       " \n",
-       "          [[ 4.2547e-03, -1.8250e-02, -3.8854e-03],\n",
-       "           [ 2.0025e-03, -9.9888e-03,  4.1697e-03],\n",
-       "           [ 1.7656e-04,  1.0839e-02,  7.3014e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.1012e-02,  6.4429e-03, -7.2240e-03],\n",
-       "           [ 1.4329e-02, -3.3623e-03,  1.2573e-02],\n",
-       "           [ 1.0066e-02,  1.9129e-04, -5.5946e-03]],\n",
-       " \n",
-       "          [[-8.3110e-03, -5.0057e-02, -1.7138e-02],\n",
-       "           [-3.0235e-02, -5.7462e-02, -2.3163e-02],\n",
-       "           [-2.9879e-02, -2.9012e-03,  1.6532e-03]],\n",
-       " \n",
-       "          [[ 1.7374e-02,  1.2611e-02,  3.3446e-02],\n",
-       "           [ 4.3700e-03,  1.4940e-03,  1.7854e-02],\n",
-       "           [-4.8574e-03, -3.0236e-03, -3.3499e-03]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2090, 0.2008, 0.1576, 0.2588, 0.1974, 0.2236, 0.2117, 0.2240, 0.2162,\n",
-       "         0.2379, 0.1668, 0.2233, 0.1864, 0.1823, 0.1916, 0.2572, 0.1698, 0.2154,\n",
-       "         0.2062, 0.1765, 0.2207, 0.1808, 0.1960, 0.2033, 0.1661, 0.2234, 0.2054,\n",
-       "         0.2515, 0.2507, 0.2516, 0.2271, 0.1719, 0.2565, 0.2240, 0.2162, 0.1895,\n",
-       "         0.1743, 0.2032, 0.2710, 0.2297, 0.2277, 0.2528, 0.2192, 0.2132, 0.2272,\n",
-       "         0.2109, 0.2510, 0.1776, 0.2540, 0.2076, 0.1371, 0.1798, 0.1567, 0.2438,\n",
-       "         0.2360, 0.2053, 0.2352, 0.1929, 0.2121, 0.1812, 0.1353, 0.2194, 0.1409,\n",
-       "         0.2954, 0.2335, 0.2070, 0.2211, 0.2000, 0.2141, 0.1989, 0.2073, 0.2366,\n",
-       "         0.2021, 0.2171, 0.2186, 0.1962, 0.2752, 0.1760, 0.0870, 0.2081, 0.1626,\n",
-       "         0.2242, 0.1984, 0.1871, 0.2665, 0.1940, 0.2113, 0.2141, 0.2287, 0.2314,\n",
-       "         0.1986, 0.2423, 0.1313, 0.2087, 0.1870, 0.1852, 0.2039, 0.1890, 0.2033,\n",
-       "         0.2101, 0.1608, 0.2100, 0.1819, 0.2230, 0.2262, 0.2116, 0.1855, 0.1821,\n",
-       "         0.2510, 0.1825, 0.2028, 0.2280, 0.1978, 0.2436, 0.2005, 0.2031, 0.2432,\n",
-       "         0.3097, 0.2462, 0.2094, 0.2291, 0.2336, 0.2736, 0.2631, 0.2043, 0.2017,\n",
-       "         0.2086, 0.2042, 0.2082, 0.2469, 0.1976, 0.1929, 0.1883, 0.2624, 0.2839,\n",
-       "         0.1647, 0.2149, 0.2006, 0.1908, 0.2296, 0.2359, 0.1825, 0.1993, 0.1244,\n",
-       "         0.1828, 0.1729, 0.1685, 0.1800, 0.1907, 0.1772, 0.2520, 0.2486, 0.1719,\n",
-       "         0.1789, 0.2217, 0.1894, 0.2161, 0.2164, 0.2201, 0.2170, 0.2480, 0.2370,\n",
-       "         0.2810, 0.1720, 0.2075, 0.1897, 0.1971, 0.1724, 0.2274, 0.2144, 0.2109,\n",
-       "         0.1504, 0.2078, 0.1818, 0.2059, 0.2303, 0.2049, 0.1753, 0.2409, 0.2112,\n",
-       "         0.2259, 0.2359, 0.2212, 0.2076, 0.2061, 0.2246, 0.2305, 0.2349, 0.2513,\n",
-       "         0.2207, 0.1367, 0.1087, 0.2146, 0.2537, 0.1974, 0.2104, 0.1699, 0.1954,\n",
-       "         0.1879, 0.1609, 0.1994, 0.2412, 0.1998, 0.2174, 0.1979, 0.2050, 0.2135,\n",
-       "         0.1782, 0.2161, 0.2443, 0.2662, 0.2724, 0.2010, 0.2070, 0.1985, 0.2079,\n",
-       "         0.1969, 0.2050, 0.2174, 0.1659, 0.1891, 0.2724, 0.1499, 0.2381, 0.1874,\n",
-       "         0.2579, 0.2569, 0.2190, 0.2145, 0.1985, 0.1965, 0.2270, 0.2129, 0.1899,\n",
-       "         0.2030, 0.1475, 0.1375, 0.2122, 0.1648, 0.1614, 0.1228, 0.2176, 0.2129,\n",
-       "         0.2501, 0.2240, 0.1914, 0.2282, 0.2512, 0.1909, 0.2217, 0.2017, 0.2267,\n",
-       "         0.1785, 0.2355, 0.1813, 0.2130], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.2286, -0.1662, -0.1720, -0.4203, -0.2529, -0.2804, -0.1909, -0.2296,\n",
-       "         -0.2210, -0.2885, -0.2117, -0.3225, -0.1331, -0.2227, -0.1555, -0.3762,\n",
-       "         -0.1673, -0.2181, -0.3037, -0.1478, -0.2093, -0.1499, -0.2754, -0.3071,\n",
-       "         -0.1086, -0.2944, -0.2411, -0.2824, -0.3160, -0.2245, -0.3463, -0.2160,\n",
-       "         -0.3144, -0.2702, -0.2498, -0.2286, -0.2181, -0.1572, -0.3645, -0.1695,\n",
-       "         -0.3038, -0.3770, -0.2083, -0.1754, -0.2737, -0.1878, -0.3008, -0.2076,\n",
-       "         -0.2930, -0.1889, -0.0329, -0.1648, -0.0956, -0.3051, -0.2201, -0.1719,\n",
-       "         -0.1883, -0.1958, -0.1897, -0.1246,  0.0325, -0.2560, -0.0692, -0.4398,\n",
-       "         -0.2477, -0.2487, -0.3030, -0.1919, -0.2654, -0.1677, -0.2318, -0.1985,\n",
-       "         -0.2235, -0.3031, -0.2045, -0.2287, -0.3423, -0.1280,  0.0783, -0.3454,\n",
-       "         -0.1554, -0.2356, -0.2173, -0.1798, -0.3094, -0.1625, -0.2791, -0.3125,\n",
-       "         -0.2807, -0.2999, -0.2467, -0.2563, -0.0743, -0.2000, -0.2364, -0.1918,\n",
-       "         -0.1877, -0.2126, -0.2781, -0.2226, -0.0779, -0.1514, -0.2294, -0.2858,\n",
-       "         -0.2517, -0.2366, -0.1531, -0.1708, -0.3420, -0.1815, -0.1926, -0.3664,\n",
-       "         -0.1894, -0.2187, -0.1894, -0.1886, -0.2776, -0.2638, -0.3011, -0.1785,\n",
-       "         -0.1500, -0.3147, -0.4400, -0.2990, -0.1724, -0.2685, -0.2463, -0.2384,\n",
-       "         -0.2022, -0.1750, -0.3041, -0.2631, -0.1800, -0.2669, -0.3498, -0.2174,\n",
-       "         -0.1967, -0.1731, -0.1557, -0.2006, -0.2225, -0.1589, -0.2394, -0.0145,\n",
-       "         -0.2485, -0.0878, -0.1637, -0.1789, -0.1388, -0.1284, -0.3016, -0.2780,\n",
-       "         -0.1589, -0.1754, -0.2208, -0.2059, -0.2279, -0.2393, -0.3136, -0.2462,\n",
-       "         -0.3556, -0.2772, -0.4293, -0.1356, -0.2561, -0.2872, -0.1644, -0.2107,\n",
-       "         -0.2398, -0.2443, -0.2015, -0.0455, -0.1695, -0.2208, -0.1680, -0.2809,\n",
-       "         -0.2531, -0.1583, -0.3244, -0.2490, -0.2698, -0.3104, -0.2524, -0.2311,\n",
-       "         -0.2650, -0.3269, -0.3174, -0.3307, -0.2638, -0.1868,  0.0083, -0.0341,\n",
-       "         -0.2163, -0.2560, -0.0989, -0.2529, -0.0425, -0.2242, -0.1380, -0.1365,\n",
-       "         -0.2088, -0.2601, -0.2275, -0.2713, -0.2355, -0.2148, -0.2607, -0.1398,\n",
-       "         -0.3350, -0.2999, -0.3852, -0.3940, -0.2061, -0.1715, -0.1720, -0.2429,\n",
-       "         -0.2105, -0.2451, -0.2306, -0.1342, -0.1223, -0.4171, -0.0767, -0.3847,\n",
-       "         -0.2299, -0.3484, -0.2693, -0.2422, -0.1991, -0.1377, -0.1997, -0.3715,\n",
-       "         -0.2805, -0.2140, -0.3186, -0.0929, -0.0395, -0.2587, -0.1291, -0.1441,\n",
-       "         -0.0412, -0.2191, -0.2105, -0.1535, -0.2840, -0.2100, -0.2658, -0.3569,\n",
-       "         -0.2713, -0.2767, -0.2433, -0.2965, -0.1675, -0.2350, -0.2200, -0.3028],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-1.9140e-02, -1.1594e-02,  6.4511e-03],\n",
-       "           [-3.0462e-03,  2.2502e-02,  5.4796e-02],\n",
-       "           [-7.3031e-03,  1.3106e-02,  4.2054e-02]],\n",
-       " \n",
-       "          [[ 2.3457e-02,  2.4577e-03,  1.5482e-03],\n",
-       "           [ 1.1015e-02, -2.8596e-03, -3.3980e-02],\n",
-       "           [-6.4870e-03, -1.2091e-02, -8.0713e-03]],\n",
-       " \n",
-       "          [[-2.3924e-02, -2.6131e-02, -6.1779e-03],\n",
-       "           [-3.3685e-02, -2.1215e-02, -1.6399e-02],\n",
-       "           [-1.3954e-02, -1.5689e-03, -2.7955e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.4406e-02, -4.0041e-02, -3.6283e-02],\n",
-       "           [ 5.7498e-03,  7.1807e-02,  1.3561e-02],\n",
-       "           [-3.8731e-03,  2.3890e-02,  4.5472e-02]],\n",
-       " \n",
-       "          [[ 1.5263e-02,  2.3057e-02,  5.4211e-03],\n",
-       "           [-6.9139e-03, -1.7966e-02, -2.5058e-02],\n",
-       "           [ 1.9139e-02,  7.2155e-03, -1.2735e-02]],\n",
-       " \n",
-       "          [[-5.1978e-03, -9.4337e-03, -7.9165e-03],\n",
-       "           [-1.2337e-02, -9.8976e-03, -4.2268e-03],\n",
-       "           [ 1.1674e-02, -2.2070e-03, -1.0403e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.7153e-02,  1.2596e-02,  2.5535e-02],\n",
-       "           [-2.7315e-02, -1.2727e-02,  2.1069e-02],\n",
-       "           [-2.5545e-02, -1.3539e-02,  1.6368e-03]],\n",
-       " \n",
-       "          [[ 2.9616e-02,  1.6828e-02,  2.3338e-02],\n",
-       "           [ 2.0518e-02,  2.0732e-02,  3.7727e-02],\n",
-       "           [-5.1533e-03, -3.7693e-03,  3.0925e-02]],\n",
-       " \n",
-       "          [[-5.9876e-03,  1.0164e-02, -2.0348e-02],\n",
-       "           [-4.0631e-03,  1.3877e-02, -2.8593e-03],\n",
-       "           [-1.8170e-02, -6.1963e-03, -4.0739e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.4245e-02,  2.2791e-03,  7.8468e-03],\n",
-       "           [-3.4345e-03, -1.4006e-02, -4.2728e-04],\n",
-       "           [ 1.5901e-03, -7.6274e-03,  6.6633e-03]],\n",
-       " \n",
-       "          [[-1.0224e-02, -8.6385e-03, -2.3865e-02],\n",
-       "           [-1.5882e-02, -1.1749e-02, -1.9457e-02],\n",
-       "           [-2.8552e-02, -2.5593e-02, -1.1550e-02]],\n",
-       " \n",
-       "          [[-8.7560e-03,  1.1242e-02, -6.2707e-03],\n",
-       "           [-4.1328e-02, -3.5372e-02, -3.6269e-02],\n",
-       "           [-5.3233e-02, -2.6381e-02, -2.8079e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.3756e-02, -9.5866e-03,  4.8648e-03],\n",
-       "           [-9.3423e-04, -1.0913e-02, -1.2969e-02],\n",
-       "           [ 1.5128e-03, -2.3616e-02, -2.6107e-02]],\n",
-       " \n",
-       "          [[ 1.2699e-02,  6.1843e-03,  1.0928e-02],\n",
-       "           [-6.5107e-03,  4.5801e-03, -5.8083e-04],\n",
-       "           [-2.0468e-02, -1.4318e-02, -1.3844e-02]],\n",
-       " \n",
-       "          [[-6.3847e-03,  4.5987e-03,  9.2693e-03],\n",
-       "           [-1.7962e-02, -8.1229e-03,  1.2352e-02],\n",
-       "           [-1.0608e-02,  1.8161e-03,  1.1116e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.2152e-02, -2.4039e-02, -3.9847e-02],\n",
-       "           [ 8.5317e-04, -9.0878e-03, -3.0101e-02],\n",
-       "           [ 1.9091e-02,  4.0882e-02,  2.6351e-02]],\n",
-       " \n",
-       "          [[-2.0745e-02, -2.3143e-02, -2.3885e-02],\n",
-       "           [-8.5238e-03, -6.1377e-03, -4.7169e-03],\n",
-       "           [ 1.2918e-02,  5.2501e-03, -7.7026e-03]],\n",
-       " \n",
-       "          [[ 4.1595e-03, -1.6786e-02, -4.4245e-04],\n",
-       "           [-1.6075e-03, -1.4638e-02, -7.1698e-03],\n",
-       "           [-1.2618e-02, -3.5132e-03, -1.5685e-03]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-9.6275e-03, -8.3084e-03, -5.8452e-03],\n",
-       "           [-1.8511e-02, -4.2882e-03,  1.2119e-03],\n",
-       "           [-6.4125e-03,  4.3035e-03, -2.7693e-03]],\n",
-       " \n",
-       "          [[-9.8499e-03, -1.8098e-02, -2.4948e-02],\n",
-       "           [-8.8643e-03, -1.2592e-02, -2.4028e-02],\n",
-       "           [ 1.0003e-03, -4.8747e-03, -7.5140e-03]],\n",
-       " \n",
-       "          [[ 1.4096e-02,  1.3290e-02, -3.0031e-03],\n",
-       "           [ 1.8351e-02,  1.3086e-02, -1.1837e-03],\n",
-       "           [ 7.0306e-03,  5.8276e-03,  4.8682e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.0342e-02,  2.5282e-02,  2.6885e-03],\n",
-       "           [ 4.2594e-03,  1.7762e-02,  8.3813e-03],\n",
-       "           [-1.4362e-02, -1.2415e-02, -8.3093e-03]],\n",
-       " \n",
-       "          [[ 8.0291e-04, -4.4494e-03, -9.2840e-03],\n",
-       "           [-8.3410e-03, -2.4109e-02, -2.6058e-02],\n",
-       "           [-6.5448e-03, -1.0334e-02, -1.0264e-02]],\n",
-       " \n",
-       "          [[-1.0848e-02, -2.8773e-03, -6.1801e-03],\n",
-       "           [-1.5541e-02, -1.3624e-02, -1.4222e-02],\n",
-       "           [-2.4975e-02, -7.9951e-03, -1.6251e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 5.8190e-03, -6.0307e-03, -6.2364e-03],\n",
-       "           [ 7.9472e-03, -1.9927e-02, -2.1147e-02],\n",
-       "           [ 1.6559e-02, -1.8161e-03, -4.1673e-03]],\n",
-       " \n",
-       "          [[-1.6912e-02, -1.8820e-02,  2.1214e-03],\n",
-       "           [ 6.5524e-03,  3.3629e-03,  1.5401e-03],\n",
-       "           [ 3.8069e-03,  1.9656e-02,  1.7377e-03]],\n",
-       " \n",
-       "          [[-8.6690e-03, -1.1540e-02, -2.2523e-02],\n",
-       "           [-9.8950e-04, -1.1504e-02, -1.5829e-02],\n",
-       "           [-1.1304e-02, -1.0738e-02, -9.9721e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 6.6224e-03,  5.3975e-02,  3.8876e-02],\n",
-       "           [ 6.7261e-03,  8.0180e-03,  4.0626e-02],\n",
-       "           [ 4.8890e-03,  2.3105e-03,  2.4165e-03]],\n",
-       " \n",
-       "          [[ 1.0881e-02, -1.1282e-03, -1.8691e-02],\n",
-       "           [-8.3147e-03, -2.8815e-02, -3.9297e-02],\n",
-       "           [-7.4877e-03, -1.3859e-02, -2.2920e-02]],\n",
-       " \n",
-       "          [[-3.6883e-03, -3.6019e-04,  1.3492e-02],\n",
-       "           [-8.3267e-03,  8.9291e-03,  2.6469e-02],\n",
-       "           [ 1.7560e-02,  4.1455e-02,  4.3970e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 2.2116e-02,  1.7553e-02,  1.2687e-02],\n",
-       "           [ 1.3144e-02,  4.7315e-03,  2.5332e-03],\n",
-       "           [ 1.4945e-02,  7.9240e-03,  6.9317e-03]],\n",
-       " \n",
-       "          [[ 3.7249e-03,  1.0365e-02,  3.9912e-03],\n",
-       "           [ 1.0039e-02,  2.1600e-02,  1.1153e-02],\n",
-       "           [ 1.2663e-02,  1.7944e-02,  7.9637e-03]],\n",
-       " \n",
-       "          [[ 2.2950e-04,  6.8045e-03,  1.0554e-02],\n",
-       "           [ 5.7162e-04,  5.7727e-04,  1.2044e-02],\n",
-       "           [-9.2145e-04,  7.8321e-04,  1.2635e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 5.5615e-03,  1.8124e-02,  2.6352e-02],\n",
-       "           [ 3.0709e-03,  9.4282e-03,  1.3110e-02],\n",
-       "           [-8.4982e-03, -2.4339e-03,  5.6546e-03]],\n",
-       " \n",
-       "          [[-2.0638e-02, -1.7595e-02, -1.2122e-02],\n",
-       "           [-1.7766e-02, -6.6617e-03, -1.9797e-04],\n",
-       "           [-9.1421e-03,  4.3421e-03,  2.7428e-03]],\n",
-       " \n",
-       "          [[ 2.2838e-03,  1.3774e-03,  2.9606e-03],\n",
-       "           [ 6.7218e-05,  3.8731e-03,  6.2144e-03],\n",
-       "           [ 2.7005e-03,  2.4869e-03,  3.7167e-03]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.4082,  0.2022,  0.1859,  0.0819,  0.2680,  0.0670,  0.2278,  0.0339,\n",
-       "          0.1028,  0.0798,  0.0699,  0.1335,  0.1981,  0.0748,  0.1312,  0.1284,\n",
-       "          0.1384,  0.0900,  0.0329,  0.0951,  0.1115,  0.1004,  0.3275,  0.1006,\n",
-       "          0.1701,  0.0843,  0.2065,  0.0804,  0.0650,  0.0969,  0.1629,  0.1490,\n",
-       "          0.2424,  0.0892,  0.2245,  0.0499,  0.1479,  0.1538,  0.1376,  0.0614,\n",
-       "          0.0697,  0.4174,  0.0409,  0.0950,  0.2041,  0.1695,  0.1150,  0.2226,\n",
-       "          0.0529,  0.0972,  0.1224,  0.0731,  0.3170,  0.1237,  0.2397,  0.0499,\n",
-       "          0.1810,  0.1325,  0.2076,  0.2289,  0.3247,  0.1153,  0.3340,  0.0766,\n",
-       "          0.1629,  0.3003,  0.1521,  0.1046,  0.2737,  0.1722,  0.1731,  0.0937,\n",
-       "          0.1287,  0.1140,  0.1388,  0.1785,  0.1278,  0.0839,  0.1184,  0.2244,\n",
-       "          0.2080,  0.2750,  0.2132,  0.0979,  0.1753,  0.0831,  0.0626,  0.2915,\n",
-       "          0.1529,  0.1354,  0.2328,  0.0172,  0.0411,  0.1279,  0.1157,  0.2245,\n",
-       "          0.1973,  0.1814,  0.0892,  0.0976,  0.1132,  0.2355,  0.2458,  0.1398,\n",
-       "          0.3905,  0.3298,  0.1582,  0.1110,  0.1777,  0.1488,  0.2175,  0.0397,\n",
-       "          0.2630,  0.2790,  0.1081,  0.1480,  0.0300,  0.1256,  0.0890,  0.1356,\n",
-       "          0.0571,  0.2368,  0.0442,  0.1963,  0.0761,  0.0683,  0.4606,  0.1357,\n",
-       "          0.1408,  0.0795,  0.1180,  0.1467,  0.2413,  0.1593,  0.2196,  0.1959,\n",
-       "          0.1105,  0.2684,  0.0935,  0.1395,  0.1128,  0.1283,  0.2092,  0.0492,\n",
-       "          0.2411,  0.2171,  0.0857,  0.1335,  0.1796,  0.0997,  0.1453,  0.3055,\n",
-       "          0.1358,  0.0627,  0.2150,  0.2297,  0.1696,  0.0830,  0.1824,  0.2712,\n",
-       "          0.3943,  0.2901,  0.3268,  0.0995,  0.1357,  0.3532,  0.2582,  0.1494,\n",
-       "          0.1936,  0.1316,  0.2370,  0.1426,  0.1605,  0.1770,  0.3755,  0.1403,\n",
-       "          0.2720,  0.1146,  0.1375,  0.1995,  0.2746,  0.3615,  0.3217,  0.2289,\n",
-       "          0.1412,  0.1386,  0.2498,  0.3350,  0.1397,  0.1180,  0.0651,  0.0855,\n",
-       "          0.0743,  0.0849,  0.0761,  0.1836,  0.2839,  0.2077,  0.1404,  0.2417,\n",
-       "          0.2992,  0.0833,  0.1398,  0.0909,  0.1070,  0.0560,  0.1810,  0.3414,\n",
-       "          0.1938,  0.2703,  0.1034,  0.1184,  0.0700,  0.0797,  0.2791,  0.2992,\n",
-       "          0.3818,  0.0945,  0.1181,  0.0553,  0.2327,  0.2301,  0.0862,  0.3070,\n",
-       "          0.2741,  0.1800,  0.0999,  0.1777,  0.1704,  0.2080,  0.1509,  0.3359,\n",
-       "          0.1060,  0.1216,  0.2879,  0.0410, -0.0411,  0.0690,  0.1738,  0.0496,\n",
-       "          0.1658,  0.1939,  0.1217,  0.1745,  0.2819,  0.1836,  0.1473,  0.2576,\n",
-       "          0.0381,  0.1069,  0.1278,  0.2932,  0.1442,  0.1058,  0.1111,  0.0415],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.3086, -0.1550, -0.0915,  0.0261, -0.1912, -0.0669, -0.2334, -0.0026,\n",
-       "         -0.0310, -0.0443, -0.0584, -0.0812, -0.0762, -0.0182, -0.0435, -0.0727,\n",
-       "         -0.0777, -0.0451, -0.0096, -0.0471, -0.0413, -0.0063, -0.1503, -0.0800,\n",
-       "         -0.1170, -0.0303,  0.0035, -0.0314, -0.0367, -0.0778, -0.1413, -0.1138,\n",
-       "         -0.1851, -0.0219, -0.1971,  0.0201,  0.0019, -0.0863,  0.0686, -0.0718,\n",
-       "         -0.0140, -0.0148, -0.0006,  0.0062, -0.1430, -0.1236, -0.0642, -0.1648,\n",
-       "         -0.0007, -0.0361, -0.0385, -0.0018, -0.1715, -0.0725, -0.1229,  0.0170,\n",
-       "         -0.1177, -0.0735, -0.1862, -0.0605, -0.1018, -0.0509, -0.2972, -0.0723,\n",
-       "         -0.1095, -0.0550, -0.0745, -0.0515, -0.1498, -0.0958, -0.1450, -0.0492,\n",
-       "          0.0092,  0.0006, -0.0953, -0.1783, -0.0417, -0.0505, -0.0275, -0.2342,\n",
-       "         -0.1201, -0.1712, -0.1582, -0.0478, -0.0017, -0.0355, -0.0077, -0.1905,\n",
-       "         -0.0816, -0.0814, -0.1526,  0.0043, -0.0152, -0.0738, -0.0887, -0.1897,\n",
-       "         -0.1294, -0.1229, -0.0338, -0.0284, -0.0366, -0.0903, -0.2482,  0.0330,\n",
-       "         -0.2004, -0.2356, -0.0481, -0.0577, -0.1225, -0.0756, -0.1477,  0.0045,\n",
-       "         -0.1848,  0.0151, -0.0381, -0.0898,  0.0038, -0.0890, -0.0271, -0.0719,\n",
-       "         -0.0275, -0.0540,  0.0093, -0.1360, -0.0397, -0.0347, -0.3696, -0.1150,\n",
-       "         -0.0878,  0.0039, -0.0750, -0.0431, -0.1544, -0.0839, -0.1438, -0.0959,\n",
-       "         -0.0631, -0.1494, -0.0486, -0.0588, -0.1011, -0.1406, -0.1536, -0.0201,\n",
-       "         -0.1203, -0.0475,  0.0033, -0.0664, -0.1589, -0.0548, -0.0630, -0.1056,\n",
-       "         -0.0872, -0.0482, -0.0224, -0.0395, -0.2212, -0.0731, -0.1507, -0.2245,\n",
-       "         -0.1984, -0.1757, -0.2752, -0.0645, -0.0253, -0.1560, -0.1616, -0.0640,\n",
-       "         -0.1801, -0.1320, -0.1963, -0.1055, -0.0563, -0.0498, -0.2307, -0.0410,\n",
-       "         -0.2412, -0.0207, -0.0730, -0.0729, -0.0717, -0.0961, -0.2292, -0.1631,\n",
-       "         -0.0994, -0.0094, -0.1800, -0.3823, -0.1028, -0.0705,  0.0082, -0.0294,\n",
-       "         -0.0529, -0.0490, -0.0429, -0.1630, -0.1392, -0.0422, -0.0890, -0.2455,\n",
-       "         -0.2574, -0.0721, -0.0842, -0.0194,  0.0356,  0.0117, -0.1080, -0.2826,\n",
-       "         -0.0552, -0.1913, -0.0902, -0.0495, -0.0094, -0.0072, -0.2797, -0.2522,\n",
-       "         -0.1548, -0.0902, -0.0714, -0.0043, -0.1415, -0.2212, -0.0477, -0.3261,\n",
-       "         -0.0947,  0.1678, -0.0821, -0.0264, -0.0940, -0.0132, -0.0522, -0.0365,\n",
-       "         -0.0714, -0.0122, -0.2763, -0.0233,  0.0247, -0.0072, -0.0836, -0.0025,\n",
-       "         -0.0816,  0.1461, -0.0755, -0.1270, -0.2770, -0.1327, -0.0514, -0.1605,\n",
-       "          0.0278, -0.0476, -0.0646, -0.3129, -0.1035,  0.0259, -0.1147,  0.0121],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-0.0079, -0.0309, -0.0377],\n",
-       "           [ 0.0144, -0.0047, -0.0204],\n",
-       "           [-0.0059,  0.0043,  0.0023]],\n",
-       " \n",
-       "          [[ 0.0045, -0.0014,  0.0013],\n",
-       "           [-0.0117,  0.0017,  0.0073],\n",
-       "           [-0.0141,  0.0033,  0.0054]],\n",
-       " \n",
-       "          [[-0.0064,  0.0038,  0.0037],\n",
-       "           [-0.0027, -0.0003, -0.0088],\n",
-       "           [-0.0058, -0.0008, -0.0068]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0139, -0.0244, -0.0277],\n",
-       "           [-0.0165, -0.0072,  0.0004],\n",
-       "           [-0.0156, -0.0015, -0.0020]],\n",
-       " \n",
-       "          [[ 0.0024,  0.0058,  0.0051],\n",
-       "           [-0.0312, -0.0386, -0.0311],\n",
-       "           [-0.0125, -0.0115, -0.0392]],\n",
-       " \n",
-       "          [[-0.0401, -0.0322, -0.0186],\n",
-       "           [-0.0099, -0.0105, -0.0190],\n",
-       "           [-0.0154, -0.0132, -0.0228]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0049, -0.0256, -0.0238],\n",
-       "           [-0.0108,  0.0260,  0.0109],\n",
-       "           [ 0.0083,  0.0555,  0.0784]],\n",
-       " \n",
-       "          [[ 0.0020,  0.0043, -0.0032],\n",
-       "           [-0.0172, -0.0337, -0.0402],\n",
-       "           [ 0.0020,  0.0037, -0.0114]],\n",
-       " \n",
-       "          [[ 0.0070,  0.0241,  0.0110],\n",
-       "           [ 0.0111,  0.0271,  0.0053],\n",
-       "           [-0.0075,  0.0030, -0.0115]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0092, -0.0036,  0.0034],\n",
-       "           [-0.0021, -0.0162, -0.0222],\n",
-       "           [-0.0133, -0.0096, -0.0079]],\n",
-       " \n",
-       "          [[-0.0097, -0.0196, -0.0054],\n",
-       "           [-0.0192, -0.0029, -0.0129],\n",
-       "           [ 0.0052,  0.0139,  0.0087]],\n",
-       " \n",
-       "          [[ 0.0197,  0.0308,  0.0287],\n",
-       "           [-0.0012,  0.0016,  0.0028],\n",
-       "           [-0.0232, -0.0141, -0.0051]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0443, -0.0287, -0.0265],\n",
-       "           [-0.0072, -0.0369, -0.0310],\n",
-       "           [-0.0004, -0.0022, -0.0116]],\n",
-       " \n",
-       "          [[-0.0121,  0.0065, -0.0316],\n",
-       "           [-0.0057, -0.0041, -0.0101],\n",
-       "           [-0.0021,  0.0061, -0.0042]],\n",
-       " \n",
-       "          [[-0.0072, -0.0027, -0.0041],\n",
-       "           [ 0.0118,  0.0415,  0.0129],\n",
-       "           [-0.0069,  0.0062, -0.0026]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0150,  0.0132,  0.0058],\n",
-       "           [ 0.0090,  0.0122,  0.0166],\n",
-       "           [ 0.0348,  0.0302,  0.0361]],\n",
-       " \n",
-       "          [[-0.0032, -0.0036,  0.0012],\n",
-       "           [-0.0010,  0.0353, -0.0055],\n",
-       "           [-0.0005,  0.0093, -0.0025]],\n",
-       " \n",
-       "          [[-0.0182, -0.0128, -0.0348],\n",
-       "           [ 0.0007,  0.0181, -0.0183],\n",
-       "           [-0.0052, -0.0094, -0.0236]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-0.0046,  0.0105,  0.0094],\n",
-       "           [-0.0045, -0.0243, -0.0039],\n",
-       "           [ 0.0021,  0.0036,  0.0086]],\n",
-       " \n",
-       "          [[-0.0146, -0.0155, -0.0057],\n",
-       "           [-0.0132, -0.0055, -0.0230],\n",
-       "           [-0.0192,  0.0058, -0.0067]],\n",
-       " \n",
-       "          [[-0.0086, -0.0013, -0.0090],\n",
-       "           [ 0.0075,  0.0208,  0.0051],\n",
-       "           [-0.0067, -0.0013, -0.0104]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0081,  0.0013, -0.0081],\n",
-       "           [ 0.0122,  0.0152,  0.0093],\n",
-       "           [ 0.0198,  0.0010,  0.0069]],\n",
-       " \n",
-       "          [[-0.0212, -0.0159, -0.0121],\n",
-       "           [-0.0045,  0.0122, -0.0019],\n",
-       "           [-0.0073,  0.0244,  0.0072]],\n",
-       " \n",
-       "          [[-0.0071,  0.0093,  0.0028],\n",
-       "           [-0.0008,  0.0049,  0.0015],\n",
-       "           [ 0.0012, -0.0158, -0.0052]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0048,  0.0078,  0.0312],\n",
-       "           [ 0.0129, -0.0140, -0.0014],\n",
-       "           [ 0.0184,  0.0069,  0.0077]],\n",
-       " \n",
-       "          [[ 0.0375, -0.0026, -0.0148],\n",
-       "           [-0.0164, -0.0040, -0.0015],\n",
-       "           [-0.0077,  0.0017, -0.0056]],\n",
-       " \n",
-       "          [[ 0.0020, -0.0082, -0.0149],\n",
-       "           [ 0.0006, -0.0178, -0.0312],\n",
-       "           [ 0.0215,  0.0004, -0.0252]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0112, -0.0042,  0.0083],\n",
-       "           [-0.0061,  0.0024,  0.0023],\n",
-       "           [-0.0071, -0.0030, -0.0088]],\n",
-       " \n",
-       "          [[ 0.0008, -0.0102, -0.0126],\n",
-       "           [ 0.0141, -0.0060, -0.0103],\n",
-       "           [ 0.0064, -0.0067, -0.0113]],\n",
-       " \n",
-       "          [[-0.0108,  0.0068,  0.0362],\n",
-       "           [-0.0288, -0.0105,  0.0096],\n",
-       "           [-0.0140, -0.0078,  0.0139]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0107, -0.0109,  0.0231],\n",
-       "           [ 0.0262,  0.0089,  0.0277],\n",
-       "           [-0.0119,  0.0264, -0.0010]],\n",
-       " \n",
-       "          [[ 0.0267,  0.0083, -0.0025],\n",
-       "           [ 0.0291,  0.0177, -0.0095],\n",
-       "           [ 0.0353,  0.0254, -0.0010]],\n",
-       " \n",
-       "          [[ 0.0166,  0.0146, -0.0086],\n",
-       "           [ 0.0198,  0.0042, -0.0228],\n",
-       "           [ 0.0074,  0.0043, -0.0108]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0079,  0.0016, -0.0272],\n",
-       "           [ 0.0034,  0.0162,  0.0291],\n",
-       "           [-0.0015, -0.0008,  0.0360]],\n",
-       " \n",
-       "          [[ 0.0151,  0.0045, -0.0061],\n",
-       "           [ 0.0087, -0.0173,  0.0140],\n",
-       "           [-0.0040, -0.0258,  0.0079]],\n",
-       " \n",
-       "          [[ 0.0423,  0.0188,  0.0156],\n",
-       "           [ 0.0427,  0.0299,  0.0160],\n",
-       "           [ 0.0340,  0.0317,  0.0146]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.1897, 0.2028, 0.2353, 0.2277, 0.2017, 0.1792, 0.1797, 0.1991, 0.2210,\n",
-       "         0.1884, 0.2044, 0.2130, 0.2555, 0.1918, 0.2096, 0.2174, 0.2034, 0.2056,\n",
-       "         0.2309, 0.2057, 0.2196, 0.2740, 0.1761, 0.2094, 0.2483, 0.1985, 0.3117,\n",
-       "         0.2738, 0.1891, 0.1854, 0.2302, 0.1907, 0.2084, 0.2080, 0.2742, 0.2583,\n",
-       "         0.2159, 0.2970, 0.2018, 0.1601, 0.2106, 0.2370, 0.1812, 0.2168, 0.2101,\n",
-       "         0.2125, 0.2567, 0.2296, 0.1870, 0.2319, 0.1406, 0.2214, 0.1888, 0.2457,\n",
-       "         0.2019, 0.1600, 0.2254, 0.2278, 0.2002, 0.5656, 0.2253, 0.2158, 0.1387,\n",
-       "         0.1654, 0.1955, 0.1897, 0.1787, 0.1922, 0.2365, 0.1817, 0.2167, 0.2565,\n",
-       "         0.1992, 0.2918, 0.2627, 0.1874, 0.1829, 0.1970, 0.2020, 0.1917, 0.2088,\n",
-       "         0.2153, 0.2239, 0.2091, 0.2379, 0.2061, 0.2370, 0.1633, 0.2096, 0.2629,\n",
-       "         0.1985, 0.2451, 0.2181, 0.2688, 0.2077, 0.1792, 0.2604, 0.1678, 0.0709,\n",
-       "         0.1819, 0.1855, 0.1982, 0.2028, 0.2220, 0.1956, 0.2424, 0.2299, 0.2244,\n",
-       "         0.2353, 0.2015, 0.2618, 0.2053, 0.1789, 0.1866, 0.1814, 0.1725, 0.2764,\n",
-       "         0.2041, 0.2448, 0.1982, 0.2196, 0.2076, 0.2062, 0.2010, 0.2007, 0.1814,\n",
-       "         0.1940, 0.1899, 0.1883, 0.2127, 0.2449, 0.1648, 0.1614, 0.2213, 0.1628,\n",
-       "         0.1798, 0.1787, 0.1723, 0.2850, 0.2173, 0.1803, 0.1633, 0.2693, 0.2028,\n",
-       "         0.2256, 0.2194, 0.2232, 0.2439, 0.1703, 0.2252, 0.2200, 0.1944, 0.1679,\n",
-       "         0.1976, 0.1470, 0.1828, 0.2713, 0.1886, 0.1954, 0.1837, 0.2826, 0.2263,\n",
-       "         0.1891, 0.1931, 0.3041, 0.1691, 0.2414, 0.2221, 0.2013, 0.1617, 0.2004,\n",
-       "         0.1857, 0.2166, 0.2317, 0.1954, 0.1773, 0.2171, 0.2207, 0.1821, 0.1377,\n",
-       "         0.2724, 0.2136, 0.2569, 0.2200, 0.2642, 0.2162, 0.1752, 0.1817, 0.1801,\n",
-       "         0.2405, 0.1784, 0.2246, 0.2054, 0.2606, 0.2179, 0.1865, 0.2174, 0.2694,\n",
-       "         0.2041, 0.2135, 0.1884, 0.1804, 0.1871, 0.1873, 0.1964, 0.1650, 0.2037,\n",
-       "         0.1612, 0.2064, 0.1879, 0.1889, 0.2478, 0.2436, 0.1698, 0.1843, 0.1780,\n",
-       "         0.1467, 0.2323, 0.1891, 0.2985, 0.1638, 0.2129, 0.2097, 0.2389, 0.2386,\n",
-       "         0.2200, 0.2465, 0.2317, 0.2030, 0.1999, 0.2470, 0.2058, 0.1408, 0.1579,\n",
-       "         0.2240, 0.1815, 0.2116, 0.1885, 0.1794, 0.2130, 0.2663, 0.2056, 0.1418,\n",
-       "         0.1220, 0.2359, 0.2717, 0.3311, 0.2402, 0.2029, 0.2482, 0.2044, 0.1448,\n",
-       "         0.1161, 0.2824, 0.2409, 0.2099], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.2129, -0.1910, -0.2709, -0.2866, -0.2705, -0.2236, -0.1657, -0.2396,\n",
-       "         -0.2896, -0.2480, -0.1647, -0.2226, -0.2527, -0.1851, -0.2281, -0.2174,\n",
-       "         -0.1931, -0.3297, -0.3219, -0.2423, -0.1894, -0.2604, -0.1866, -0.2826,\n",
-       "         -0.2816, -0.1927, -0.4853, -0.3498, -0.1480, -0.1605, -0.3149, -0.2137,\n",
-       "         -0.1626, -0.2297, -0.3960, -0.3537, -0.2210, -0.4863, -0.2438, -0.0916,\n",
-       "         -0.2363, -0.2808, -0.2578, -0.3048, -0.1984, -0.1892, -0.3654, -0.2667,\n",
-       "         -0.1882, -0.2863, -0.0290, -0.3132, -0.1471, -0.2516, -0.2288, -0.0712,\n",
-       "         -0.2082, -0.2919, -0.3011, -0.6515, -0.2208, -0.2728,  0.0043, -0.1328,\n",
-       "         -0.2704, -0.2172, -0.1809, -0.1648, -0.3754, -0.1695, -0.2127, -0.3258,\n",
-       "         -0.2243, -0.4164, -0.4195, -0.1917, -0.1532, -0.2492, -0.1406, -0.2138,\n",
-       "         -0.2657, -0.2805, -0.2194, -0.2602, -0.1872, -0.2556, -0.2329, -0.1399,\n",
-       "         -0.1864, -0.2851, -0.2576, -0.3207, -0.2796, -0.4270, -0.1931, -0.1765,\n",
-       "         -0.3562, -0.1476,  0.1235, -0.1422, -0.1549, -0.2713, -0.3063, -0.2212,\n",
-       "         -0.2097, -0.2340, -0.2433, -0.2547, -0.2164, -0.2095, -0.2896, -0.2115,\n",
-       "         -0.1750, -0.2764, -0.1706, -0.1236, -0.4356, -0.1944, -0.2847, -0.2250,\n",
-       "         -0.2395, -0.2200, -0.2244, -0.1960, -0.1889, -0.1436, -0.2150, -0.2822,\n",
-       "         -0.1619, -0.2703, -0.2986, -0.1564, -0.1223, -0.2320, -0.0498, -0.1451,\n",
-       "         -0.3158, -0.1373, -0.3093, -0.2771, -0.2165, -0.1940, -0.3744, -0.3010,\n",
-       "         -0.2451, -0.2049, -0.2376, -0.2995, -0.1485, -0.2253, -0.2388, -0.1775,\n",
-       "         -0.1483, -0.2308, -0.0789, -0.1867, -0.3321, -0.2104, -0.1976, -0.1677,\n",
-       "         -0.3640, -0.2853, -0.2217, -0.1595, -0.3344, -0.1877, -0.2989, -0.2851,\n",
-       "         -0.2320, -0.1570, -0.2330, -0.1777, -0.2628, -0.2230, -0.1810, -0.0774,\n",
-       "         -0.2346, -0.2671, -0.1862, -0.0345, -0.3244, -0.2897, -0.3741, -0.2525,\n",
-       "         -0.2786, -0.2837, -0.1694, -0.2203, -0.1574, -0.2993, -0.1952, -0.2856,\n",
-       "         -0.2165, -0.3927, -0.2681, -0.1898, -0.2693, -0.3980, -0.1731, -0.2384,\n",
-       "         -0.2290, -0.2196, -0.2116, -0.1463, -0.1676, -0.1194, -0.2124, -0.1185,\n",
-       "         -0.2012, -0.1410, -0.1773, -0.2963, -0.3288, -0.1854, -0.2128, -0.2164,\n",
-       "         -0.0280, -0.2934, -0.2153, -0.3816, -0.0770, -0.2346, -0.1934, -0.2025,\n",
-       "         -0.3792, -0.2692, -0.2916, -0.2673, -0.2913, -0.2849, -0.2746, -0.2758,\n",
-       "         -0.0773, -0.1998, -0.2500, -0.1641, -0.2109, -0.2059, -0.1553, -0.2239,\n",
-       "         -0.2733, -0.1864, -0.0474, -0.0367, -0.2387, -0.2924, -0.5615, -0.2852,\n",
-       "         -0.2191, -0.3182, -0.1692, -0.0384, -0.0637, -0.4191, -0.2466, -0.2119],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 0.0060,  0.0091,  0.0014],\n",
-       "           [ 0.0270,  0.0150,  0.0112],\n",
-       "           [ 0.0066, -0.0083,  0.0048]],\n",
-       " \n",
-       "          [[-0.0230, -0.0265, -0.0071],\n",
-       "           [ 0.0244,  0.0368,  0.0005],\n",
-       "           [-0.0005,  0.0156, -0.0013]],\n",
-       " \n",
-       "          [[ 0.0031, -0.0233,  0.0006],\n",
-       "           [ 0.0054,  0.0057,  0.0171],\n",
-       "           [ 0.0189,  0.0226,  0.0334]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0203,  0.0106,  0.0057],\n",
-       "           [-0.0024, -0.0049, -0.0113],\n",
-       "           [-0.0272, -0.0144, -0.0258]],\n",
-       " \n",
-       "          [[ 0.0015, -0.0005, -0.0164],\n",
-       "           [ 0.0072, -0.0048, -0.0124],\n",
-       "           [-0.0028, -0.0118,  0.0076]],\n",
-       " \n",
-       "          [[-0.0141, -0.0227, -0.0084],\n",
-       "           [ 0.0111, -0.0152,  0.0052],\n",
-       "           [ 0.0225, -0.0286, -0.0289]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0053, -0.0074, -0.0032],\n",
-       "           [ 0.0019, -0.0151, -0.0069],\n",
-       "           [-0.0098, -0.0298, -0.0147]],\n",
-       " \n",
-       "          [[ 0.0171,  0.0210,  0.0425],\n",
-       "           [ 0.0126,  0.0106,  0.0291],\n",
-       "           [ 0.0055,  0.0061,  0.0166]],\n",
-       " \n",
-       "          [[ 0.0163,  0.0281,  0.0080],\n",
-       "           [-0.0057,  0.0244,  0.0151],\n",
-       "           [-0.0169, -0.0011,  0.0011]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0108, -0.0253, -0.0193],\n",
-       "           [-0.0064, -0.0144, -0.0149],\n",
-       "           [-0.0083, -0.0099, -0.0099]],\n",
-       " \n",
-       "          [[-0.0198, -0.0162,  0.0044],\n",
-       "           [-0.0337, -0.0285, -0.0153],\n",
-       "           [-0.0149, -0.0480, -0.0166]],\n",
-       " \n",
-       "          [[ 0.0039, -0.0255, -0.0313],\n",
-       "           [-0.0343, -0.0448, -0.0282],\n",
-       "           [-0.0500, -0.0625, -0.0166]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0124,  0.0016,  0.0047],\n",
-       "           [ 0.0026, -0.0044, -0.0124],\n",
-       "           [ 0.0183,  0.0138,  0.0087]],\n",
-       " \n",
-       "          [[-0.0032, -0.0013, -0.0032],\n",
-       "           [-0.0115, -0.0258, -0.0168],\n",
-       "           [-0.0137, -0.0262, -0.0252]],\n",
-       " \n",
-       "          [[-0.0236, -0.0172, -0.0036],\n",
-       "           [-0.0202, -0.0130, -0.0059],\n",
-       "           [-0.0010, -0.0049,  0.0185]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0052, -0.0047, -0.0163],\n",
-       "           [ 0.0041,  0.0004, -0.0003],\n",
-       "           [-0.0076, -0.0086, -0.0141]],\n",
-       " \n",
-       "          [[-0.0080, -0.0236, -0.0328],\n",
-       "           [-0.0154, -0.0341, -0.0299],\n",
-       "           [-0.0065, -0.0255, -0.0243]],\n",
-       " \n",
-       "          [[ 0.0131, -0.0111,  0.0017],\n",
-       "           [-0.0010, -0.0253,  0.0171],\n",
-       "           [-0.0131, -0.0161,  0.0173]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0047, -0.0013,  0.0041],\n",
-       "           [-0.0052, -0.0199, -0.0165],\n",
-       "           [-0.0185, -0.0391, -0.0193]],\n",
-       " \n",
-       "          [[ 0.0018, -0.0093, -0.0125],\n",
-       "           [-0.0109, -0.0088, -0.0192],\n",
-       "           [ 0.0142,  0.0319,  0.0121]],\n",
-       " \n",
-       "          [[ 0.0292,  0.0267,  0.0088],\n",
-       "           [ 0.0231,  0.0098,  0.0211],\n",
-       "           [ 0.0213,  0.0093,  0.0069]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0044,  0.0038,  0.0171],\n",
-       "           [-0.0012,  0.0018,  0.0098],\n",
-       "           [ 0.0015,  0.0005, -0.0001]],\n",
-       " \n",
-       "          [[ 0.0135,  0.0154,  0.0132],\n",
-       "           [ 0.0154,  0.0049, -0.0057],\n",
-       "           [ 0.0211,  0.0008, -0.0261]],\n",
-       " \n",
-       "          [[ 0.0183,  0.0085, -0.0021],\n",
-       "           [ 0.0087,  0.0101,  0.0027],\n",
-       "           [-0.0072,  0.0183,  0.0254]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0130, -0.0346, -0.0202],\n",
-       "           [-0.0126, -0.0304, -0.0116],\n",
-       "           [ 0.0010, -0.0041,  0.0024]],\n",
-       " \n",
-       "          [[ 0.0134, -0.0023, -0.0039],\n",
-       "           [-0.0274, -0.0310, -0.0162],\n",
-       "           [-0.0201, -0.0180, -0.0092]],\n",
-       " \n",
-       "          [[ 0.0142,  0.0014,  0.0075],\n",
-       "           [ 0.0301,  0.0144,  0.0289],\n",
-       "           [ 0.0169,  0.0133,  0.0226]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0197, -0.0133, -0.0092],\n",
-       "           [ 0.0058, -0.0067, -0.0107],\n",
-       "           [ 0.0106,  0.0120,  0.0029]],\n",
-       " \n",
-       "          [[-0.0071, -0.0003, -0.0080],\n",
-       "           [-0.0222,  0.0171,  0.0173],\n",
-       "           [ 0.0096,  0.0163,  0.0044]],\n",
-       " \n",
-       "          [[ 0.0161,  0.0070,  0.0033],\n",
-       "           [ 0.0080,  0.0010,  0.0188],\n",
-       "           [-0.0059,  0.0085,  0.0379]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0049,  0.0051,  0.0103],\n",
-       "           [ 0.0026,  0.0026,  0.0048],\n",
-       "           [-0.0054, -0.0033, -0.0034]],\n",
-       " \n",
-       "          [[ 0.0129,  0.0154,  0.0137],\n",
-       "           [ 0.0176,  0.0235,  0.0200],\n",
-       "           [ 0.0046,  0.0098,  0.0149]],\n",
-       " \n",
-       "          [[-0.0006,  0.0022,  0.0080],\n",
-       "           [-0.0090, -0.0098,  0.0050],\n",
-       "           [ 0.0028,  0.0030,  0.0045]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0049, -0.0131, -0.0103],\n",
-       "           [-0.0067, -0.0161, -0.0118],\n",
-       "           [-0.0072, -0.0135, -0.0090]],\n",
-       " \n",
-       "          [[-0.0064, -0.0073, -0.0027],\n",
-       "           [-0.0092, -0.0032,  0.0125],\n",
-       "           [-0.0208, -0.0120,  0.0054]],\n",
-       " \n",
-       "          [[-0.0292, -0.0187, -0.0219],\n",
-       "           [-0.0310, -0.0176, -0.0302],\n",
-       "           [-0.0263, -0.0124, -0.0257]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.3179,  0.1968,  0.1120,  0.0202,  0.2900,  0.0924,  0.2252,  0.0668,\n",
-       "          0.1037,  0.0727,  0.0970,  0.0617,  0.1572,  0.0733,  0.1271,  0.1260,\n",
-       "          0.2062,  0.0853,  0.0702,  0.1037,  0.1172,  0.0912,  0.3324,  0.1068,\n",
-       "          0.1725,  0.0689,  0.1892,  0.1383, -0.0465,  0.1016,  0.2127,  0.1289,\n",
-       "          0.2854,  0.0800,  0.2692,  0.0716,  0.1522,  0.1691,  0.0121,  0.1015,\n",
-       "          0.1096,  0.4042,  0.0264,  0.1204,  0.1940,  0.1640,  0.1274,  0.2779,\n",
-       "          0.0355,  0.0913,  0.1079,  0.0922,  0.2893,  0.1549,  0.3214,  0.0406,\n",
-       "          0.1429,  0.1328,  0.2154,  0.2671,  0.2809,  0.1154,  0.3424,  0.1001,\n",
-       "          0.1761,  0.2640,  0.1681,  0.1134,  0.2647,  0.1588,  0.1898,  0.0701,\n",
-       "          0.0890,  0.1219,  0.1223,  0.2126,  0.1407,  0.1045,  0.0927,  0.1963,\n",
-       "          0.1882,  0.3396,  0.2499,  0.1396,  0.1200,  0.0797,  0.0868,  0.3942,\n",
-       "          0.1662,  0.1254,  0.1686,  0.0472,  0.0437,  0.0645,  0.1162,  0.2049,\n",
-       "          0.1250,  0.2159,  0.0775,  0.0528,  0.1050,  0.2375,  0.3881,  0.1893,\n",
-       "          0.3760,  0.3620,  0.1396,  0.1586,  0.1474,  0.1057,  0.2409,  0.0728,\n",
-       "          0.4409,  0.2468,  0.1014,  0.1437,  0.0394,  0.1599,  0.1045,  0.1194,\n",
-       "          0.0469,  0.2259,  0.0445,  0.2615,  0.0997,  0.0593,  0.4668,  0.1249,\n",
-       "          0.1228,  0.0901,  0.1295,  0.1371,  0.2117,  0.1670,  0.1968,  0.1813,\n",
-       "          0.1036,  0.2689,  0.0808,  0.1255,  0.1109,  0.1029,  0.2275,  0.0410,\n",
-       "          0.2402,  0.2234,  0.0815,  0.1698,  0.2166,  0.0704,  0.1669,  0.3272,\n",
-       "          0.1079,  0.1022,  0.1910,  0.2534,  0.1443,  0.1072,  0.2329,  0.3026,\n",
-       "          0.3550,  0.1949,  0.3964,  0.1336,  0.1170,  0.3885,  0.2671,  0.1346,\n",
-       "          0.2538,  0.1428,  0.2042,  0.1417,  0.1253,  0.1423,  0.3138,  0.1541,\n",
-       "          0.3170,  0.1015,  0.2175,  0.1795,  0.3712,  0.3298,  0.4602,  0.2513,\n",
-       "          0.1838,  0.1855,  0.2774,  0.4398,  0.0988,  0.1186,  0.0776,  0.0996,\n",
-       "          0.0873,  0.1284,  0.0760,  0.2201,  0.2185,  0.2499,  0.1181,  0.2516,\n",
-       "          0.3899,  0.0965,  0.1965,  0.1058,  0.0827,  0.0324,  0.1785,  0.1915,\n",
-       "          0.0746,  0.3624,  0.1118,  0.1106,  0.1017,  0.0637,  0.2720,  0.3615,\n",
-       "          0.3098,  0.1284,  0.1362,  0.0542,  0.2810,  0.2628,  0.0635,  0.2424,\n",
-       "          0.2259,  0.0980,  0.1071,  0.2446,  0.1317,  0.2907,  0.1594,  0.4031,\n",
-       "          0.1048,  0.1153,  0.3532,  0.0623,  0.0390,  0.0972,  0.1997,  0.0381,\n",
-       "          0.1511,  0.2328,  0.1155,  0.1957,  0.3090,  0.1537,  0.1340,  0.2276,\n",
-       "          0.0617,  0.1247,  0.1132,  0.3145,  0.0613,  0.1128,  0.1025,  0.0483],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-1.9669e-01, -1.1889e-01, -7.2699e-02,  5.4707e-03, -2.3972e-01,\n",
-       "         -8.3967e-02,  5.1151e-02, -3.0668e-02, -6.2054e-02, -5.0522e-02,\n",
-       "         -7.5919e-02, -6.3343e-02, -1.2994e-01, -3.3344e-02, -7.6683e-02,\n",
-       "         -1.0125e-01, -1.7178e-01, -3.4106e-02, -2.7688e-02, -7.1105e-02,\n",
-       "         -7.7762e-02, -2.8178e-02, -2.6341e-01, -7.3303e-02, -7.7106e-02,\n",
-       "         -5.6329e-02, -7.5045e-02, -4.7186e-02,  1.0246e-02, -1.0432e-01,\n",
-       "         -2.4359e-01, -1.1178e-01, -1.7658e-01, -7.5498e-02, -1.1965e-01,\n",
-       "          4.6700e-03, -1.2447e-01, -1.2801e-01, -1.8694e-02, -1.0271e-01,\n",
-       "         -3.1512e-02, -1.3195e-01, -6.7434e-03, -3.4661e-02, -1.7058e-01,\n",
-       "         -7.5562e-02, -1.0965e-01, -1.9147e-01, -1.3373e-02, -5.8812e-02,\n",
-       "         -4.0793e-02, -5.6996e-02, -2.7747e-01, -1.4008e-01, -2.2675e-01,\n",
-       "          6.8755e-03, -1.3716e-01, -6.4051e-02, -1.7344e-01, -1.3398e-01,\n",
-       "         -2.0955e-01, -7.9926e-02, -3.1990e-01, -1.1105e-01, -1.5112e-01,\n",
-       "         -2.6487e-01, -1.3937e-01, -8.9252e-02, -7.9651e-02, -2.1113e-01,\n",
-       "         -1.5560e-01, -2.4231e-02, -2.1112e-02, -7.7139e-02, -7.7369e-02,\n",
-       "         -1.8874e-01, -9.5576e-02, -7.1680e-02, -1.9991e-02, -2.2632e-01,\n",
-       "         -1.0990e-01, -1.8879e-01, -2.8083e-01, -1.1230e-01, -5.3369e-04,\n",
-       "         -6.6425e-02, -4.3774e-02, -2.6541e-01, -1.4741e-01, -8.3975e-02,\n",
-       "         -1.4655e-01, -1.7254e-03, -2.1871e-02, -4.4119e-02, -9.3119e-02,\n",
-       "         -1.8546e-01, -5.4063e-02, -1.9519e-01, -4.8110e-02, -1.9600e-02,\n",
-       "         -4.8454e-02, -1.8013e-01, -2.7370e-01,  8.0736e-03, -3.0815e-01,\n",
-       "         -2.9727e-01, -9.0802e-02, -9.4406e-02, -6.0626e-02, -9.9645e-02,\n",
-       "         -1.1133e-01, -8.1866e-03, -3.9501e-01, -1.4449e-01, -3.1214e-02,\n",
-       "         -1.2697e-01,  2.2461e-03, -1.6903e-01, -7.5954e-02, -6.9719e-02,\n",
-       "         -2.8950e-02, -1.1662e-01, -8.6935e-03, -1.7185e-01, -7.3293e-02,\n",
-       "         -4.6802e-02, -3.5859e-01, -1.1228e-01, -1.0186e-01, -1.3286e-02,\n",
-       "         -1.1683e-01, -6.6500e-02, -1.4019e-01,  9.1050e-02, -2.2624e-01,\n",
-       "         -1.0020e-01, -8.7691e-02, -1.9472e-01, -3.6835e-02, -8.6403e-02,\n",
-       "         -1.9938e-02,  5.6991e-02, -1.6266e-01, -5.3330e-02, -2.1087e-01,\n",
-       "         -2.7770e-01, -1.4534e-02, -1.3407e-01, -1.2097e-01, -3.5311e-04,\n",
-       "         -9.8875e-02, -2.1248e-01, -7.7204e-02, -8.0924e-02, -1.1657e-01,\n",
-       "         -2.0229e-01,  3.4251e-02, -1.0850e-01, -1.6650e-01, -2.6503e-01,\n",
-       "         -2.4252e-01, -6.2257e-02, -2.5988e-01, -6.9636e-02, -6.1391e-02,\n",
-       "         -2.0767e-01, -2.1611e-01, -4.4427e-02, -1.9276e-01, -1.4739e-01,\n",
-       "         -2.1940e-01, -8.4459e-02,  1.3927e-03, -6.7973e-02, -2.8062e-01,\n",
-       "         -1.4142e-01, -1.6462e-01, -4.5306e-02, -1.6903e-01, -1.6244e-01,\n",
-       "         -1.2978e-01, -3.6487e-01, -3.0622e-01, -1.8519e-01, -1.2948e-01,\n",
-       "          1.6085e-02, -2.2938e-01, -3.6094e-01, -8.0586e-02, -7.3673e-02,\n",
-       "         -5.2966e-02, -5.0699e-02, -1.0142e-01, -7.4486e-02, -5.9100e-02,\n",
-       "         -1.5923e-01, -1.3740e-01,  9.6897e-02, -5.0817e-02, -2.1771e-01,\n",
-       "         -2.4404e-01, -6.4470e-02, -1.4574e-01, -9.5731e-02, -3.1563e-02,\n",
-       "         -1.1076e-02, -6.9732e-02, -1.8957e-01, -5.3666e-02, -1.4674e-01,\n",
-       "         -2.5673e-02, -7.7109e-02, -3.9023e-02, -2.3153e-02, -2.9631e-01,\n",
-       "         -3.4795e-01, -1.7564e-01, -1.2596e-01, -1.1269e-01, -1.0919e-02,\n",
-       "         -1.8710e-01, -2.2720e-01, -4.9506e-02, -2.1385e-01, -1.2587e-01,\n",
-       "         -2.5868e-02, -1.1558e-01, -9.6342e-02, -1.0061e-01, -1.4629e-01,\n",
-       "         -1.2946e-01, -1.5277e-01, -3.4629e-02, -7.2215e-02, -3.3424e-01,\n",
-       "         -4.1791e-02,  3.5658e-03, -1.1674e-01, -1.2922e-01, -2.1173e-02,\n",
-       "         -5.4626e-02,  9.4908e-02, -4.4153e-02, -1.5585e-01, -2.0149e-01,\n",
-       "         -1.2752e-01, -1.2001e-01, -1.3880e-01,  7.1416e-03, -5.8071e-02,\n",
-       "         -3.0546e-02, -2.9134e-01, -5.6794e-02, -8.5133e-02, -1.2527e-01,\n",
-       "          9.4942e-04], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 5.9139e-03, -2.4779e-03, -1.5367e-02],\n",
-       "           [ 1.2542e-02,  1.9493e-02,  1.1076e-02],\n",
-       "           [-2.4200e-02, -1.8346e-02, -1.7958e-02]],\n",
-       " \n",
-       "          [[-1.2121e-02, -5.2124e-03, -1.2557e-02],\n",
-       "           [-1.2245e-02, -1.8570e-02, -1.5383e-02],\n",
-       "           [ 1.3546e-02,  3.0554e-04,  5.7610e-03]],\n",
-       " \n",
-       "          [[ 6.2826e-03,  4.4313e-03, -5.2499e-04],\n",
-       "           [ 1.8552e-02,  1.6414e-02,  2.5396e-03],\n",
-       "           [ 6.0778e-03,  9.0545e-03, -3.8498e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.6496e-03, -1.4232e-03, -9.9060e-03],\n",
-       "           [ 8.1273e-03,  1.6145e-03, -2.5942e-03],\n",
-       "           [-5.4927e-03,  1.0256e-02,  4.2581e-03]],\n",
-       " \n",
-       "          [[-4.5449e-03, -2.0329e-02, -2.8247e-02],\n",
-       "           [-1.1547e-02, -1.2362e-02, -6.1297e-03],\n",
-       "           [ 3.2779e-03,  9.6488e-03,  2.3592e-02]],\n",
-       " \n",
-       "          [[ 1.1146e-02,  1.5417e-02,  1.2016e-02],\n",
-       "           [ 2.3711e-03,  3.6488e-03,  8.4299e-03],\n",
-       "           [-1.7472e-02, -1.3502e-02, -1.0677e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 2.2769e-02,  1.8094e-02,  6.5215e-03],\n",
-       "           [-1.5094e-02, -2.1315e-02, -2.4300e-02],\n",
-       "           [-1.7483e-02, -1.8787e-02, -1.7867e-02]],\n",
-       " \n",
-       "          [[-6.7647e-03, -2.0062e-02,  1.4148e-02],\n",
-       "           [ 3.5913e-04, -9.2430e-03,  2.1406e-02],\n",
-       "           [ 8.9242e-03, -1.8819e-02,  4.6946e-03]],\n",
-       " \n",
-       "          [[ 4.0819e-03, -8.6351e-03, -2.8246e-03],\n",
-       "           [ 1.9474e-02,  9.4520e-03,  4.5882e-03],\n",
-       "           [ 1.5090e-02,  1.0167e-02,  4.8051e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.6375e-02,  7.0200e-03, -1.2540e-02],\n",
-       "           [ 1.8899e-02,  1.4540e-02, -7.6896e-03],\n",
-       "           [ 2.7228e-02,  2.6327e-02,  1.2170e-02]],\n",
-       " \n",
-       "          [[-1.3029e-02, -3.6729e-02,  1.5025e-03],\n",
-       "           [ 1.5931e-02,  4.8525e-02,  3.0864e-02],\n",
-       "           [ 1.9271e-02,  6.5164e-02,  3.0848e-02]],\n",
-       " \n",
-       "          [[-1.9414e-02, -3.4001e-03, -1.7802e-02],\n",
-       "           [-4.5313e-04,  1.5943e-02, -5.7809e-03],\n",
-       "           [-1.5090e-03,  3.5900e-03, -7.5772e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.4183e-02,  6.6220e-03, -1.5423e-02],\n",
-       "           [-1.9527e-04, -9.6526e-03, -1.1083e-02],\n",
-       "           [ 1.3118e-02,  1.4465e-02,  2.7204e-02]],\n",
-       " \n",
-       "          [[-1.9476e-02, -4.4989e-02, -2.1759e-03],\n",
-       "           [-2.5099e-02, -3.7157e-02,  8.6706e-04],\n",
-       "           [-5.5323e-03, -1.9652e-02, -1.3641e-03]],\n",
-       " \n",
-       "          [[-1.0250e-02, -1.0732e-02,  7.6957e-03],\n",
-       "           [-7.6875e-03, -7.1187e-03,  3.6033e-03],\n",
-       "           [ 1.4880e-02,  2.4076e-02,  1.6152e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-7.9649e-03, -2.4387e-02, -3.0268e-02],\n",
-       "           [-2.2641e-02, -2.2236e-02, -2.6668e-02],\n",
-       "           [-1.7855e-02, -1.8200e-02, -3.4379e-03]],\n",
-       " \n",
-       "          [[ 1.5081e-02,  4.9373e-02, -3.0204e-02],\n",
-       "           [ 9.6058e-03,  1.3778e-01, -1.8774e-02],\n",
-       "           [-4.2983e-02, -1.9699e-02, -5.1577e-02]],\n",
-       " \n",
-       "          [[-3.3637e-02, -3.7505e-02, -1.8569e-02],\n",
-       "           [-1.5347e-02, -8.4495e-03, -8.9501e-03],\n",
-       "           [-9.7927e-05,  2.5400e-03, -1.6047e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-2.7828e-03,  1.5242e-02,  1.7564e-03],\n",
-       "           [ 3.9431e-03, -1.6584e-02, -1.3783e-02],\n",
-       "           [ 5.5669e-03,  1.3504e-03,  6.4934e-03]],\n",
-       " \n",
-       "          [[-2.7473e-03, -1.5878e-02, -1.3850e-02],\n",
-       "           [ 1.2929e-02, -1.2897e-02, -1.7508e-03],\n",
-       "           [ 3.0639e-02, -7.3981e-03,  5.5625e-03]],\n",
-       " \n",
-       "          [[-4.1312e-03, -1.6526e-02, -1.7142e-02],\n",
-       "           [-3.3078e-04, -7.0226e-03, -4.4350e-03],\n",
-       "           [-1.4639e-03,  2.4660e-03,  4.5799e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 3.1357e-02,  1.8496e-02, -1.7394e-03],\n",
-       "           [ 1.8550e-02,  9.4947e-03, -6.2801e-03],\n",
-       "           [ 3.1567e-02,  1.7903e-02,  1.0166e-02]],\n",
-       " \n",
-       "          [[ 6.2833e-03,  4.0168e-03, -9.5135e-03],\n",
-       "           [ 3.0477e-03, -2.6772e-04, -2.2587e-03],\n",
-       "           [ 2.9213e-03,  2.5891e-02,  1.1125e-02]],\n",
-       " \n",
-       "          [[ 1.4020e-02,  5.9374e-03,  5.8125e-03],\n",
-       "           [ 4.2242e-03, -5.3491e-03,  1.3486e-03],\n",
-       "           [-1.0059e-02, -2.7544e-02, -3.0324e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-4.4319e-02, -2.4118e-02,  1.3717e-02],\n",
-       "           [-2.8995e-02, -3.2137e-02, -2.4759e-02],\n",
-       "           [ 8.7138e-03,  2.5874e-03, -3.6108e-03]],\n",
-       " \n",
-       "          [[-1.9986e-02,  1.0908e-02,  2.8119e-02],\n",
-       "           [-2.4283e-02, -2.1204e-02, -4.2846e-03],\n",
-       "           [-1.5918e-02, -2.1474e-02, -5.9793e-03]],\n",
-       " \n",
-       "          [[ 8.0179e-03,  1.2452e-02,  2.8599e-02],\n",
-       "           [ 1.8087e-03,  1.8180e-02,  2.5374e-02],\n",
-       "           [ 1.4116e-04,  3.6753e-03,  3.0858e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.7412e-02, -2.9829e-03,  3.3054e-02],\n",
-       "           [-1.7834e-03,  9.7694e-03,  1.8624e-02],\n",
-       "           [-1.0896e-02,  6.6576e-03, -1.4011e-02]],\n",
-       " \n",
-       "          [[-2.6590e-02,  4.2138e-03,  7.5583e-03],\n",
-       "           [-3.0090e-02, -3.3097e-03,  4.5168e-02],\n",
-       "           [-6.8074e-03, -1.5288e-02, -2.7594e-02]],\n",
-       " \n",
-       "          [[-9.8640e-03,  2.7229e-03,  7.0191e-03],\n",
-       "           [-1.7108e-03, -9.4750e-03, -7.7426e-03],\n",
-       "           [-1.3215e-02, -2.4027e-02, -1.7200e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-6.2178e-03,  1.2235e-02, -1.2392e-03],\n",
-       "           [ 5.0869e-03,  1.6927e-02,  2.1139e-03],\n",
-       "           [ 8.1385e-04, -1.3492e-02, -5.9440e-03]],\n",
-       " \n",
-       "          [[-2.0009e-02, -1.2719e-02,  2.0989e-03],\n",
-       "           [-2.8298e-02, -2.1533e-02,  4.1090e-03],\n",
-       "           [ 6.7198e-03,  1.0331e-02,  9.3100e-03]],\n",
-       " \n",
-       "          [[-3.3087e-03,  8.5076e-03, -8.1201e-03],\n",
-       "           [ 1.5631e-02,  3.1521e-02,  1.4243e-02],\n",
-       "           [ 4.6865e-03,  1.9334e-02,  4.3085e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 8.9894e-03, -1.1848e-02, -2.2767e-02],\n",
-       "           [ 1.2436e-03,  6.5014e-03,  1.0327e-02],\n",
-       "           [ 1.4480e-02,  1.8107e-02,  1.0400e-02]],\n",
-       " \n",
-       "          [[-2.3718e-02, -2.7665e-02, -1.7171e-02],\n",
-       "           [-2.1747e-02, -2.2288e-02, -1.1905e-02],\n",
-       "           [-1.9754e-02,  4.0819e-03,  2.2568e-03]],\n",
-       " \n",
-       "          [[-2.2110e-02, -2.1952e-02, -1.9803e-02],\n",
-       "           [-1.8152e-02, -2.9595e-02, -1.8346e-02],\n",
-       "           [-1.8463e-02, -2.0269e-02, -1.9519e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2974, 0.1953, 0.2646, 0.2384, 0.2324, 0.2408, 0.2109, 0.2494, 0.2280,\n",
-       "         0.2144, 0.1952, 0.1591, 0.2013, 0.2185, 0.1832, 0.2386, 0.2527, 0.2202,\n",
-       "         0.2689, 0.2403, 0.2413, 0.2457, 0.2441, 0.2264, 0.2065, 0.2390, 0.2816,\n",
-       "         0.2229, 0.2263, 0.2383, 0.1978, 0.2122, 0.2186, 0.1972, 0.2102, 0.1988,\n",
-       "         0.1857, 0.2778, 0.2039, 0.2142, 0.1919, 0.2351, 0.2727, 0.1729, 0.1924,\n",
-       "         0.2337, 0.2201, 0.2279, 0.1907, 0.2109, 0.2569, 0.2103, 0.2257, 0.2178,\n",
-       "         0.2354, 0.2219, 0.2339, 0.2070, 0.1939, 0.2286, 0.2323, 0.2247, 0.2305,\n",
-       "         0.2343, 0.2153, 0.2140, 0.2152, 0.1992, 0.2246, 0.2002, 0.2737, 0.2582,\n",
-       "         0.2421, 0.2499, 0.2289, 0.2709, 0.1856, 0.2261, 0.2243, 0.2500, 0.2599,\n",
-       "         0.2321, 0.2144, 0.2149, 0.2101, 0.2165, 0.2145, 0.2229, 0.2043, 0.2284,\n",
-       "         0.2182, 0.2117, 0.2558, 0.2244, 0.2807, 0.2210, 0.1779, 0.2766, 0.2022,\n",
-       "         0.2021, 0.2642, 0.2043, 0.2378, 0.2165, 0.2367, 0.2112, 0.2343, 0.2148,\n",
-       "         0.2085, 0.2354, 0.1960, 0.2012, 0.2405, 0.2408, 0.2785, 0.2201, 0.2289,\n",
-       "         0.2775, 0.2282, 0.2222, 0.1857, 0.1847, 0.2312, 0.2261, 0.1851, 0.2291,\n",
-       "         0.1637, 0.2405, 0.1804, 0.1978, 0.2515, 0.2030, 0.2332, 0.1513, 0.2123,\n",
-       "         0.2264, 0.2217, 0.2255, 0.2777, 0.2506, 0.2152, 0.1813, 0.1862, 0.2021,\n",
-       "         0.2532, 0.2299, 0.1889, 0.2410, 0.2363, 0.1964, 0.2173, 0.1988, 0.1953,\n",
-       "         0.2414, 0.2258, 0.2271, 0.1910, 0.1774, 0.2402, 0.2167, 0.2269, 0.2109,\n",
-       "         0.2681, 0.2181, 0.2733, 0.2221, 0.2004, 0.1949, 0.2521, 0.1827, 0.1841,\n",
-       "         0.2021, 0.2197, 0.2118, 0.2124, 0.2048, 0.2443, 0.2148, 0.1569, 0.2847,\n",
-       "         0.2044, 0.2122, 0.2637, 0.2180, 0.2796, 0.2784, 0.1773, 0.2353, 0.2433,\n",
-       "         0.2505, 0.2224, 0.2478, 0.2059, 0.2294, 0.2818, 0.1983, 0.2461, 0.3039,\n",
-       "         0.1900, 0.2560, 0.2248, 0.2159, 0.2491, 0.1983, 0.2000, 0.2069, 0.2392,\n",
-       "         0.2788, 0.2365, 0.2173, 0.1970, 0.2397, 0.2093, 0.2583, 0.2045, 0.2428,\n",
-       "         0.2375, 0.2229, 0.2546, 0.1912, 0.2626, 0.2148, 0.2236, 0.2168, 0.2070,\n",
-       "         0.2291, 0.2074, 0.2145, 0.2209, 0.2138, 0.1993, 0.2369, 0.1896, 0.2492,\n",
-       "         0.2512, 0.2453, 0.2146, 0.2360, 0.2341, 0.2314, 0.2402, 0.2321, 0.2424,\n",
-       "         0.2236, 0.2176, 0.2248, 0.2179, 0.2419, 0.2429, 0.1868, 0.1844, 0.1927,\n",
-       "         0.2752, 0.2502, 0.2443, 0.2197], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.2637, -0.2985, -0.3583, -0.2689, -0.2365, -0.2700, -0.2070, -0.2421,\n",
-       "         -0.3279, -0.1879, -0.2475, -0.0700, -0.2176, -0.2516, -0.1671, -0.1406,\n",
-       "         -0.2773, -0.2417, -0.2830, -0.2124, -0.2979, -0.2533, -0.3451, -0.2060,\n",
-       "         -0.2283, -0.1899, -0.3699, -0.1317, -0.1534, -0.2481, -0.1481, -0.1844,\n",
-       "         -0.1584, -0.2458, -0.2006, -0.2604, -0.0417, -0.4505, -0.2034, -0.2141,\n",
-       "         -0.1919, -0.2857, -0.2870, -0.1816, -0.1762, -0.2619, -0.2743, -0.1967,\n",
-       "         -0.1385, -0.2217, -0.2907, -0.2674, -0.2252, -0.1852, -0.2806, -0.2309,\n",
-       "         -0.2439, -0.2328, -0.2182, -0.2225, -0.2405, -0.2286, -0.2585, -0.2974,\n",
-       "         -0.2644, -0.1893, -0.1251, -0.2244, -0.1593, -0.1586, -0.2552, -0.2303,\n",
-       "         -0.2103, -0.2667, -0.2720, -0.3082, -0.1653, -0.3035, -0.2402, -0.2320,\n",
-       "         -0.2654, -0.2727, -0.2251, -0.2913, -0.2403, -0.2237, -0.2924, -0.2689,\n",
-       "         -0.2259, -0.2504, -0.2132, -0.1466, -0.3044, -0.1732, -0.3290, -0.2759,\n",
-       "         -0.0505, -0.2766, -0.2143, -0.2523, -0.3112, -0.2683, -0.1843, -0.1949,\n",
-       "         -0.1744, -0.1582, -0.2599, -0.2357, -0.1927, -0.2263, -0.1409, -0.2263,\n",
-       "         -0.2830, -0.2591, -0.4128, -0.2136, -0.2459, -0.3436, -0.2524, -0.2186,\n",
-       "         -0.1406, -0.1832, -0.2117, -0.1713, -0.1459, -0.2187, -0.0693, -0.2801,\n",
-       "         -0.1530, -0.2183, -0.2680, -0.1376, -0.2701, -0.1029, -0.1489, -0.1554,\n",
-       "         -0.2242, -0.2770, -0.2618, -0.2457, -0.2274, -0.2178, -0.1557, -0.1617,\n",
-       "         -0.1877, -0.2013, -0.1541, -0.2566, -0.2660, -0.1533, -0.1886, -0.2156,\n",
-       "         -0.1955, -0.2256, -0.2331, -0.2282, -0.1739, -0.1645, -0.1333, -0.1952,\n",
-       "         -0.2077, -0.2051, -0.1979, -0.2174, -0.3622, -0.2710, -0.1346, -0.1569,\n",
-       "         -0.3214, -0.2267, -0.1514, -0.1828, -0.2985, -0.2424, -0.2340, -0.0919,\n",
-       "         -0.2947, -0.1641, -0.0497, -0.2708, -0.2689, -0.2372, -0.2649, -0.2433,\n",
-       "         -0.3351, -0.2108, -0.1656, -0.2528, -0.3073, -0.2521, -0.1669, -0.2352,\n",
-       "         -0.3134, -0.2168, -0.3349, -0.1479, -0.3528, -0.4337, -0.2044, -0.2914,\n",
-       "         -0.2626, -0.2324, -0.2793, -0.1647, -0.1299, -0.2142, -0.2660, -0.2231,\n",
-       "         -0.2999, -0.2507, -0.2373, -0.2808, -0.1661, -0.2988, -0.2115, -0.1849,\n",
-       "         -0.3022, -0.1817, -0.1911, -0.2617, -0.3104, -0.2444, -0.2825, -0.1831,\n",
-       "         -0.1697, -0.2051, -0.1818, -0.2646, -0.2397, -0.1413, -0.1167, -0.2809,\n",
-       "         -0.0696, -0.2014, -0.2347, -0.2760, -0.1848, -0.2306, -0.1645, -0.2767,\n",
-       "         -0.2122, -0.2588, -0.2890, -0.1959, -0.1616, -0.2656, -0.2070, -0.2222,\n",
-       "         -0.3201, -0.2094, -0.1442, -0.2799, -0.3060, -0.2569, -0.2736, -0.2717],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 6.7145e-03,  1.8146e-02,  9.1501e-03],\n",
-       "           [-4.8132e-04,  3.9244e-04,  4.9019e-03],\n",
-       "           [-3.9935e-03, -1.5253e-02, -1.9431e-03]],\n",
-       " \n",
-       "          [[-1.3909e-02, -1.1909e-02, -3.3332e-03],\n",
-       "           [-3.2293e-02, -4.2058e-02, -1.4386e-02],\n",
-       "           [-1.6054e-02, -2.7204e-02, -8.0264e-03]],\n",
-       " \n",
-       "          [[-1.4366e-03, -3.7105e-04,  1.0592e-02],\n",
-       "           [-8.3351e-03,  1.5678e-02,  1.1996e-02],\n",
-       "           [ 2.7395e-02,  4.0255e-02,  4.3797e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.4377e-02, -1.3785e-02, -2.1212e-02],\n",
-       "           [-1.4473e-03,  9.5810e-03, -1.5244e-02],\n",
-       "           [ 7.5819e-03,  2.9882e-02,  8.6469e-03]],\n",
-       " \n",
-       "          [[ 1.2909e-02,  7.3969e-03, -2.1414e-02],\n",
-       "           [ 8.8873e-03,  1.9162e-02, -2.0900e-02],\n",
-       "           [-2.4488e-02,  3.1306e-03, -1.5526e-03]],\n",
-       " \n",
-       "          [[-8.0866e-03, -2.5685e-02, -2.1394e-03],\n",
-       "           [-2.9957e-02, -6.4925e-02, -1.8534e-02],\n",
-       "           [ 8.2515e-03, -2.3108e-02,  5.0956e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.0769e-03, -3.9713e-03,  6.1909e-04],\n",
-       "           [-5.0586e-03, -5.7226e-03, -3.0978e-03],\n",
-       "           [-9.7060e-03, -4.9222e-03, -1.0522e-02]],\n",
-       " \n",
-       "          [[-4.2681e-02, -2.9429e-02, -1.6531e-02],\n",
-       "           [-2.3635e-02,  9.8182e-04, -9.2204e-05],\n",
-       "           [-3.9383e-02, -3.2026e-02, -3.4666e-02]],\n",
-       " \n",
-       "          [[-7.8516e-03, -5.5814e-03,  1.9045e-02],\n",
-       "           [-3.5366e-05, -2.5253e-02, -1.3229e-02],\n",
-       "           [-1.7166e-02, -3.1123e-02, -1.7749e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-8.3461e-03, -1.8412e-02, -1.3952e-02],\n",
-       "           [-3.0128e-02, -1.5758e-02, -1.7667e-03],\n",
-       "           [-1.6984e-02,  7.5203e-04,  2.1886e-02]],\n",
-       " \n",
-       "          [[-1.8384e-02,  1.1373e-03,  1.3364e-02],\n",
-       "           [-1.3377e-02, -3.2809e-04, -2.4089e-03],\n",
-       "           [-6.9000e-03,  6.5661e-03, -9.6617e-03]],\n",
-       " \n",
-       "          [[-3.6913e-03, -7.6579e-03, -9.1248e-03],\n",
-       "           [-5.8181e-03, -9.4978e-03, -4.7794e-03],\n",
-       "           [ 1.5287e-02,  1.1194e-02,  1.3444e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 3.3949e-02,  5.8223e-02,  5.3213e-02],\n",
-       "           [ 2.4436e-02,  4.7512e-02,  4.2866e-02],\n",
-       "           [ 4.1178e-02,  6.5390e-02,  5.4135e-02]],\n",
-       " \n",
-       "          [[-1.5750e-02, -1.2439e-02, -5.7624e-03],\n",
-       "           [-3.9231e-03,  8.3286e-03,  1.0461e-02],\n",
-       "           [-1.3987e-02, -4.8393e-03,  6.9200e-04]],\n",
-       " \n",
-       "          [[-5.9071e-03, -5.3877e-03, -9.9656e-03],\n",
-       "           [-1.3804e-02, -1.1419e-02, -1.4199e-02],\n",
-       "           [-1.1837e-03, -4.3263e-03, -7.6042e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.1737e-02,  7.3387e-03,  1.6538e-02],\n",
-       "           [-1.2868e-02, -1.1595e-02, -1.4021e-02],\n",
-       "           [-1.3704e-02, -2.3401e-02, -9.7469e-03]],\n",
-       " \n",
-       "          [[-7.2707e-03,  3.3193e-03,  1.4713e-03],\n",
-       "           [ 1.4625e-02,  1.1683e-02,  2.2178e-02],\n",
-       "           [ 3.3535e-02,  3.1682e-02,  2.1540e-02]],\n",
-       " \n",
-       "          [[-3.4493e-03, -2.0129e-03, -3.1155e-03],\n",
-       "           [ 1.1922e-02,  1.1915e-02,  1.2004e-02],\n",
-       "           [ 1.5041e-02,  1.1674e-02,  1.4948e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 1.0883e-02,  3.7436e-03, -8.2854e-03],\n",
-       "           [ 2.1957e-02,  1.3219e-02, -6.4831e-03],\n",
-       "           [ 1.5702e-03, -2.0866e-03, -1.1087e-02]],\n",
-       " \n",
-       "          [[-2.3814e-03,  1.0184e-02, -4.8753e-04],\n",
-       "           [-1.7110e-02, -1.9477e-02, -1.0411e-02],\n",
-       "           [-1.5466e-02, -3.0986e-02, -1.2114e-02]],\n",
-       " \n",
-       "          [[-9.6246e-04, -1.1516e-02, -7.1925e-03],\n",
-       "           [-3.6171e-03,  1.5534e-02,  1.8416e-02],\n",
-       "           [-1.8437e-02, -9.9123e-03, -1.5818e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 8.7614e-04,  3.5854e-03,  4.0523e-03],\n",
-       "           [-4.5738e-03, -4.4067e-03,  1.7411e-02],\n",
-       "           [ 8.3446e-03,  3.0228e-02,  3.5163e-02]],\n",
-       " \n",
-       "          [[ 2.2027e-03, -6.5092e-03, -1.0054e-02],\n",
-       "           [ 3.8097e-02, -1.7498e-02, -1.0793e-02],\n",
-       "           [ 3.7786e-02, -3.8948e-03, -6.9825e-03]],\n",
-       " \n",
-       "          [[-7.6655e-03, -2.4622e-02, -2.1496e-02],\n",
-       "           [-1.3416e-02, -2.0270e-02, -1.8466e-02],\n",
-       "           [-2.5517e-03, -6.0563e-03, -5.1532e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-4.0166e-03, -1.0563e-02, -4.0910e-03],\n",
-       "           [-1.4096e-02, -1.7425e-02, -1.7925e-02],\n",
-       "           [ 3.5333e-03, -1.3649e-02,  5.3122e-03]],\n",
-       " \n",
-       "          [[-1.1077e-03,  2.7369e-04,  3.5609e-03],\n",
-       "           [-2.3507e-03, -8.5441e-04,  1.0273e-02],\n",
-       "           [-5.3503e-03, -9.7567e-03, -3.3135e-04]],\n",
-       " \n",
-       "          [[-2.7539e-02,  1.0827e-02, -7.5516e-03],\n",
-       "           [-1.8267e-02,  1.9708e-02,  2.2137e-02],\n",
-       "           [-1.0520e-02,  2.7239e-02, -1.3878e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 9.2865e-03,  1.7527e-02,  1.4443e-02],\n",
-       "           [-5.0630e-03, -4.0809e-03,  1.1438e-02],\n",
-       "           [-6.9689e-03, -1.4748e-02, -1.2921e-03]],\n",
-       " \n",
-       "          [[ 6.2302e-03, -1.0200e-02, -5.9622e-03],\n",
-       "           [-3.2108e-02, -3.3147e-03,  8.9811e-04],\n",
-       "           [-2.9387e-02, -2.1630e-02, -1.7457e-02]],\n",
-       " \n",
-       "          [[-1.3113e-02, -1.6402e-02, -1.2327e-02],\n",
-       "           [-5.7922e-03, -1.2582e-02, -6.2841e-03],\n",
-       "           [ 6.8716e-03,  1.1870e-02,  7.3456e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 7.8497e-03, -2.0397e-03,  6.8470e-03],\n",
-       "           [ 7.7719e-03, -4.0880e-04,  3.0701e-03],\n",
-       "           [ 2.3148e-03, -1.0455e-02, -4.7136e-03]],\n",
-       " \n",
-       "          [[ 2.5724e-03,  3.2802e-03,  2.4329e-03],\n",
-       "           [ 5.5900e-03,  7.0913e-03,  2.8156e-03],\n",
-       "           [ 4.8052e-03,  1.3661e-02,  5.6246e-03]],\n",
-       " \n",
-       "          [[ 6.0176e-03,  1.2900e-02,  1.5189e-02],\n",
-       "           [ 3.3151e-03,  4.4356e-03,  9.8302e-03],\n",
-       "           [ 1.8229e-04,  5.5495e-03,  4.7808e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-4.9424e-03, -5.5103e-03, -5.1826e-03],\n",
-       "           [-1.0206e-03,  1.8686e-03,  2.4285e-04],\n",
-       "           [-1.4398e-03,  1.0942e-03,  5.3111e-03]],\n",
-       " \n",
-       "          [[-1.5662e-02, -4.8114e-03, -1.7249e-02],\n",
-       "           [-1.0477e-02, -4.5154e-03, -1.6575e-02],\n",
-       "           [ 2.8100e-03, -9.0114e-06, -8.3807e-03]],\n",
-       " \n",
-       "          [[ 1.0011e-02,  1.1988e-02,  9.5828e-03],\n",
-       "           [ 3.7473e-03,  3.8290e-03,  6.2816e-04],\n",
-       "           [ 6.7291e-03,  3.7023e-04,  1.8696e-04]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.3643,  0.2412,  0.1630, -0.0156,  0.2918,  0.1195,  0.1542,  0.0760,\n",
-       "          0.1523,  0.0855,  0.1091,  0.1335,  0.2320,  0.0932,  0.1788,  0.1951,\n",
-       "          0.1725,  0.1252,  0.1320,  0.1356,  0.1200,  0.1408,  0.3579,  0.1565,\n",
-       "          0.1670,  0.0913,  0.2368,  0.1859,  0.0680,  0.2209,  0.2523,  0.1491,\n",
-       "          0.2228,  0.1113,  0.2374,  0.1351,  0.1481,  0.1747,  0.0612,  0.1907,\n",
-       "          0.1395,  0.2845,  0.0631,  0.1261,  0.2161,  0.2017,  0.2025,  0.2918,\n",
-       "          0.0459,  0.1338,  0.1627,  0.1285,  0.2901,  0.1694,  0.2064,  0.0573,\n",
-       "          0.2137,  0.1587,  0.2014,  0.2546,  0.2765,  0.1987,  0.3500,  0.1617,\n",
-       "          0.1817,  0.2305,  0.2009,  0.1466,  0.2582,  0.1476,  0.2070,  0.0828,\n",
-       "          0.1452,  0.1418,  0.1433,  0.2450,  0.1740,  0.1072,  0.0694,  0.2538,\n",
-       "          0.1708,  0.3028,  0.2872,  0.1486,  0.1356,  0.1250,  0.2169,  0.3134,\n",
-       "          0.2210,  0.2127,  0.2084,  0.0615,  0.0824,  0.1244,  0.1465,  0.2268,\n",
-       "          0.1680,  0.2310,  0.1298,  0.0922,  0.1334,  0.2565,  0.3667,  0.2004,\n",
-       "          0.3614,  0.3750,  0.1958,  0.1302,  0.1719,  0.1508,  0.2226,  0.0851,\n",
-       "          0.3330,  0.3050,  0.1187,  0.1762,  0.0327,  0.2615,  0.1579,  0.1033,\n",
-       "          0.0744,  0.2202, -0.1183,  0.2531,  0.1376,  0.0489,  0.3918,  0.1452,\n",
-       "          0.1196,  0.0942,  0.1696,  0.1222,  0.2339,  0.1436,  0.1583,  0.2322,\n",
-       "          0.1120,  0.2886,  0.0595,  0.1054,  0.1102,  0.1174,  0.2138,  0.1133,\n",
-       "          0.2351,  0.2733,  0.0847,  0.1669,  0.2165,  0.1267,  0.1587,  0.3431,\n",
-       "          0.1302,  0.1936,  0.2143,  0.2700,  0.1234,  0.1238,  0.2304,  0.3169,\n",
-       "          0.3105,  0.2047,  0.3448,  0.1594,  0.1293,  0.2879,  0.3211,  0.1397,\n",
-       "          0.2284,  0.1595,  0.2574,  0.1270,  0.1745,  0.1708,  0.3438,  0.1602,\n",
-       "          0.2479,  0.1899,  0.1903,  0.1780,  0.2404,  0.2887,  0.4129,  0.2572,\n",
-       "          0.1524,  0.1796,  0.2940,  0.4290,  0.1572,  0.1454,  0.2051,  0.1808,\n",
-       "          0.0890,  0.2444,  0.1362,  0.1959,  0.2150,  0.2387,  0.1956,  0.2510,\n",
-       "          0.3430,  0.1077,  0.1782,  0.1539,  0.0924, -0.0283,  0.1367,  0.2076,\n",
-       "          0.0912,  0.3074,  0.1957,  0.1317,  0.1694,  0.0819,  0.3078,  0.3540,\n",
-       "          0.2834,  0.1931,  0.1644,  0.0535,  0.2645,  0.2122,  0.0795,  0.1862,\n",
-       "          0.2474,  0.1255,  0.1455,  0.2367,  0.1741,  0.2802,  0.1635,  0.3698,\n",
-       "          0.1383,  0.1037,  0.3121,  0.0826,  0.0222,  0.1631,  0.2096,  0.1011,\n",
-       "          0.2129,  0.1925,  0.1607,  0.1803,  0.2620,  0.1989,  0.1715,  0.2238,\n",
-       "          0.0588,  0.1204,  0.1452,  0.3667,  0.1104,  0.2201,  0.1654,  0.0294],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-3.2199e-01, -1.9127e-01,  3.3287e-02, -5.1268e-03, -3.2826e-01,\n",
-       "          3.1884e-02, -1.1939e-01, -5.9890e-02, -1.7116e-01, -1.0846e-01,\n",
-       "         -1.2911e-01, -1.6430e-01, -2.1389e-01, -9.0841e-02, -1.9747e-01,\n",
-       "         -2.1466e-01, -2.1214e-01, -1.1693e-01, -1.8037e-01, -1.4891e-01,\n",
-       "         -1.4429e-01, -9.9319e-02, -5.0500e-01, -1.8350e-01, -1.2011e-01,\n",
-       "         -1.2801e-01, -1.8672e-01, -2.1630e-01, -1.0285e-02, -1.9321e-01,\n",
-       "         -2.2003e-01, -1.6935e-01, -3.4413e-01, -1.1678e-01, -1.4952e-01,\n",
-       "          2.4520e-02, -1.2921e-01, -2.2584e-01,  6.6835e-03, -2.4661e-01,\n",
-       "         -1.4645e-01, -3.5774e-01, -2.6487e-02,  1.6042e-02, -2.2357e-01,\n",
-       "         -1.7416e-01, -2.2503e-01, -2.6137e-01, -2.7927e-02, -1.3231e-01,\n",
-       "         -1.5477e-01, -1.2850e-01, -3.4516e-01, -2.4374e-01, -1.9581e-01,\n",
-       "         -3.3456e-02, -2.1683e-01, -1.4477e-01, -2.4388e-01, -2.5746e-01,\n",
-       "         -1.7628e-01, -2.1824e-01, -2.7733e-01, -2.3034e-01, -2.4605e-01,\n",
-       "         -2.3442e-01, -2.1940e-01, -8.6686e-02, -2.1605e-01, -5.0968e-02,\n",
-       "         -2.4559e-01, -9.9395e-02, -1.0503e-01, -2.0866e-01, -1.8088e-01,\n",
-       "         -2.1712e-01, -1.8659e-01, -1.6065e-01, -8.3152e-02, -3.0794e-01,\n",
-       "         -2.1155e-01, -3.7351e-01, -2.5455e-01, -1.7090e-01, -5.6925e-02,\n",
-       "         -1.2859e-01, -2.1482e-01, -3.0590e-01, -1.3277e-01, -2.4757e-01,\n",
-       "         -2.2383e-01, -2.4280e-02, -9.2163e-02, -1.3360e-01, -1.9924e-01,\n",
-       "         -2.1297e-01, -1.6504e-01, -2.4687e-01, -1.5296e-01, -5.0619e-03,\n",
-       "         -1.4516e-01, -2.6298e-01, -3.3783e-01, -1.1673e-01, -3.7430e-01,\n",
-       "         -4.8975e-01, -1.6119e-01, -7.8866e-02, -1.5747e-01, -1.8993e-01,\n",
-       "         -2.1176e-01, -5.3297e-02,  1.0738e-01, -2.1821e-01, -7.3801e-02,\n",
-       "         -1.9640e-01, -1.9422e-02, -3.1303e-01, -1.8990e-01, -4.1843e-02,\n",
-       "         -6.1171e-02, -2.3230e-01, -1.0055e-01, -2.2728e-01, -1.6532e-01,\n",
-       "         -4.2504e-02, -2.3682e-01, -1.9855e-01, -1.5340e-01, -5.8001e-02,\n",
-       "         -2.0679e-01, -1.5966e-01, -1.8889e-01, -5.4872e-02, -2.2782e-01,\n",
-       "         -1.5943e-01, -1.5651e-01, -3.3215e-01, -5.9737e-02, -1.3782e-01,\n",
-       "         -8.4768e-02, -4.7837e-02, -2.2910e-01, -1.0013e-01, -1.2656e-01,\n",
-       "         -3.3028e-01, -2.2583e-02, -2.4448e-01, -1.9557e-01, -9.7757e-02,\n",
-       "         -1.3454e-01, -3.4292e-01, -1.3548e-01, -2.2910e-01, -1.7775e-01,\n",
-       "         -2.6555e-01, -4.3564e-03, -1.1329e-01, -2.0769e-01, -3.1908e-01,\n",
-       "         -3.2258e-01, -6.8114e-02, -4.0560e-01, -1.7845e-01, -1.4233e-01,\n",
-       "         -4.3576e-01, -3.1308e-01, -1.5997e-01, -1.7272e-01, -2.0843e-01,\n",
-       "         -1.9097e-01, -1.3703e-01, -1.1651e-01, -1.6285e-01, -3.4463e-01,\n",
-       "         -1.7171e-01, -2.5748e-01, -1.9888e-01, -2.0913e-01, -2.0836e-01,\n",
-       "         -1.2004e-01, -1.9495e-01, -3.9751e-01, -2.7461e-01, -2.1816e-01,\n",
-       "         -9.4526e-02, -3.0281e-01, -4.2019e-01, -1.6623e-01, -1.5898e-01,\n",
-       "         -2.0673e-01, -1.5045e-01,  2.6478e-02, -2.8993e-01, -1.6723e-01,\n",
-       "         -1.9769e-01, -2.4568e-01, -2.2102e-01, -1.9134e-01, -2.5466e-01,\n",
-       "         -3.9838e-01, -1.3557e-01, -8.5998e-02, -1.5312e-01, -1.4045e-01,\n",
-       "          1.9907e-03, -1.5137e-01, -2.3441e-01, -9.0604e-02, -1.6758e-01,\n",
-       "         -2.1844e-01, -9.1077e-02, -2.1047e-01, -2.6563e-02, -2.4610e-01,\n",
-       "         -3.2311e-01, -2.6821e-01, -2.6571e-01, -2.3496e-01, -4.8549e-02,\n",
-       "         -3.8473e-01, -2.3681e-01, -9.9990e-02, -2.3783e-01, -2.1455e-01,\n",
-       "         -1.2394e-01,  9.2119e-03, -2.4074e-01,  3.4292e-02, -2.6152e-01,\n",
-       "          2.4179e-04, -1.8276e-01, -1.5122e-01, -1.5142e-01, -3.5635e-01,\n",
-       "         -7.2465e-02, -4.3192e-02, -2.0485e-01, -1.8568e-01, -6.5356e-02,\n",
-       "         -2.2074e-01, -4.9714e-02, -1.3650e-01, -2.4013e-01, -2.5592e-01,\n",
-       "         -2.4974e-01, -1.7180e-01, -2.3290e-01, -9.9241e-03, -1.4533e-01,\n",
-       "         -1.4245e-01, -3.5678e-01, -1.4369e-01, -2.9002e-01, -2.4482e-01,\n",
-       "         -1.2251e-02], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 0.0135,  0.0359,  0.0526],\n",
-       "           [ 0.0141,  0.0076,  0.0215],\n",
-       "           [-0.0062,  0.0123,  0.0093]],\n",
-       " \n",
-       "          [[ 0.0071, -0.0197, -0.0136],\n",
-       "           [-0.0200, -0.0276, -0.0120],\n",
-       "           [ 0.0080, -0.0118, -0.0029]],\n",
-       " \n",
-       "          [[-0.0081, -0.0050,  0.0096],\n",
-       "           [ 0.0024,  0.0015,  0.0080],\n",
-       "           [ 0.0004, -0.0054,  0.0010]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0202, -0.0430, -0.0435],\n",
-       "           [-0.0129, -0.0298, -0.0445],\n",
-       "           [-0.0066, -0.0166, -0.0158]],\n",
-       " \n",
-       "          [[ 0.0119,  0.0231, -0.0021],\n",
-       "           [ 0.0079, -0.0016, -0.0116],\n",
-       "           [-0.0036, -0.0068, -0.0199]],\n",
-       " \n",
-       "          [[-0.0041,  0.0166,  0.0100],\n",
-       "           [-0.0077,  0.0031, -0.0065],\n",
-       "           [-0.0086,  0.0010, -0.0044]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0286,  0.0285,  0.0322],\n",
-       "           [ 0.0116, -0.0088,  0.0062],\n",
-       "           [ 0.0147,  0.0308,  0.0060]],\n",
-       " \n",
-       "          [[-0.0144, -0.0128, -0.0008],\n",
-       "           [-0.0069, -0.0252, -0.0256],\n",
-       "           [-0.0151, -0.0245, -0.0085]],\n",
-       " \n",
-       "          [[ 0.0005,  0.0040,  0.0255],\n",
-       "           [ 0.0302,  0.0286,  0.0449],\n",
-       "           [ 0.0115,  0.0215,  0.0182]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0041, -0.0040,  0.0089],\n",
-       "           [-0.0053,  0.0125, -0.0151],\n",
-       "           [-0.0026,  0.0010, -0.0079]],\n",
-       " \n",
-       "          [[-0.0049, -0.0268, -0.0172],\n",
-       "           [-0.0219, -0.0611, -0.0539],\n",
-       "           [ 0.0023, -0.0443, -0.0327]],\n",
-       " \n",
-       "          [[-0.0500, -0.0508, -0.0427],\n",
-       "           [-0.0339, -0.0258, -0.0152],\n",
-       "           [-0.0316, -0.0216, -0.0051]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0201, -0.0317, -0.0374],\n",
-       "           [-0.0314, -0.0269, -0.0232],\n",
-       "           [-0.0276, -0.0200, -0.0078]],\n",
-       " \n",
-       "          [[-0.0065,  0.0140,  0.0100],\n",
-       "           [ 0.0036,  0.0080,  0.0187],\n",
-       "           [ 0.0097,  0.0166, -0.0006]],\n",
-       " \n",
-       "          [[-0.0167, -0.0152, -0.0351],\n",
-       "           [ 0.0027,  0.0009,  0.0012],\n",
-       "           [ 0.0224,  0.0268,  0.0173]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0079, -0.0008, -0.0085],\n",
-       "           [-0.0089, -0.0125, -0.0063],\n",
-       "           [ 0.0122, -0.0163, -0.0057]],\n",
-       " \n",
-       "          [[ 0.0050,  0.0099,  0.0245],\n",
-       "           [-0.0015,  0.0047,  0.0161],\n",
-       "           [ 0.0062,  0.0142, -0.0015]],\n",
-       " \n",
-       "          [[-0.0247, -0.0067, -0.0048],\n",
-       "           [-0.0076,  0.0191,  0.0080],\n",
-       "           [-0.0024,  0.0128, -0.0033]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0442,  0.0488,  0.0246],\n",
-       "           [ 0.0244,  0.0249, -0.0002],\n",
-       "           [-0.0062, -0.0037,  0.0017]],\n",
-       " \n",
-       "          [[-0.0018, -0.0059, -0.0099],\n",
-       "           [ 0.0014, -0.0085, -0.0119],\n",
-       "           [-0.0022,  0.0062, -0.0108]],\n",
-       " \n",
-       "          [[ 0.0070,  0.0071,  0.0191],\n",
-       "           [-0.0160, -0.0154, -0.0148],\n",
-       "           [-0.0103, -0.0117, -0.0141]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0367,  0.0412,  0.0225],\n",
-       "           [ 0.0279,  0.0356,  0.0192],\n",
-       "           [-0.0159,  0.0003,  0.0025]],\n",
-       " \n",
-       "          [[-0.0071, -0.0083, -0.0034],\n",
-       "           [-0.0089, -0.0184, -0.0234],\n",
-       "           [ 0.0060, -0.0045, -0.0139]],\n",
-       " \n",
-       "          [[ 0.0125,  0.0176,  0.0218],\n",
-       "           [-0.0081, -0.0098, -0.0027],\n",
-       "           [-0.0125, -0.0195, -0.0192]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0037,  0.0065,  0.0157],\n",
-       "           [-0.0132, -0.0110, -0.0077],\n",
-       "           [-0.0155, -0.0288, -0.0241]],\n",
-       " \n",
-       "          [[-0.0035, -0.0048,  0.0028],\n",
-       "           [-0.0032, -0.0081, -0.0209],\n",
-       "           [-0.0108, -0.0033, -0.0191]],\n",
-       " \n",
-       "          [[-0.0036, -0.0110, -0.0111],\n",
-       "           [-0.0150, -0.0118, -0.0052],\n",
-       "           [-0.0241, -0.0109, -0.0236]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0107,  0.0207,  0.0262],\n",
-       "           [-0.0016,  0.0472,  0.0471],\n",
-       "           [-0.0135, -0.0178, -0.0070]],\n",
-       " \n",
-       "          [[-0.0088,  0.0090,  0.0045],\n",
-       "           [-0.0203, -0.0268, -0.0177],\n",
-       "           [-0.0116, -0.0176, -0.0289]],\n",
-       " \n",
-       "          [[-0.0231, -0.0088, -0.0207],\n",
-       "           [ 0.0019,  0.0095, -0.0006],\n",
-       "           [ 0.0009,  0.0061,  0.0115]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0209, -0.0047, -0.0297],\n",
-       "           [-0.0090, -0.0270, -0.0319],\n",
-       "           [-0.0196, -0.0113, -0.0229]],\n",
-       " \n",
-       "          [[-0.0029, -0.0028,  0.0055],\n",
-       "           [-0.0035,  0.0037, -0.0003],\n",
-       "           [-0.0058, -0.0059,  0.0060]],\n",
-       " \n",
-       "          [[ 0.0145,  0.0348,  0.0506],\n",
-       "           [ 0.0047,  0.0114,  0.0107],\n",
-       "           [-0.0144, -0.0104, -0.0094]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0166,  0.0021, -0.0026],\n",
-       "           [ 0.0026,  0.0031,  0.0224],\n",
-       "           [-0.0034, -0.0027,  0.0235]],\n",
-       " \n",
-       "          [[ 0.0150,  0.0111,  0.0161],\n",
-       "           [-0.0084, -0.0044, -0.0069],\n",
-       "           [-0.0071, -0.0217, -0.0146]],\n",
-       " \n",
-       "          [[-0.0131, -0.0250, -0.0169],\n",
-       "           [ 0.0056,  0.0030, -0.0037],\n",
-       "           [ 0.0042,  0.0175,  0.0072]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2730, 0.2644, 0.2648, 0.2735, 0.2513, 0.2331, 0.2000, 0.2658, 0.2481,\n",
-       "         0.2449, 0.2674, 0.2677, 0.3125, 0.2975, 0.2252, 0.2731, 0.2170, 0.2384,\n",
-       "         0.2560, 0.2691, 0.2719, 0.2765, 0.1398, 0.2306, 0.2571, 0.2561, 0.2818,\n",
-       "         0.2530, 0.2284, 0.2561, 0.2329, 0.2344, 0.2731, 0.2488, 0.2582, 0.2755,\n",
-       "         0.2602, 0.2845, 0.2799, 0.2727, 0.2286, 0.2663, 0.2537, 0.2581, 0.2105,\n",
-       "         0.2210, 0.1919, 0.2702, 0.2288, 0.2824, 0.2797, 0.2687, 0.2337, 0.2259,\n",
-       "         0.2236, 0.2619, 0.2123, 0.2252, 0.2915, 0.2913, 0.2419, 0.2334, 0.2391,\n",
-       "         0.2038, 0.2730, 0.2410, 0.2365, 0.3007, 0.2689, 0.2821, 0.2341, 0.2489,\n",
-       "         0.2220, 0.2690, 0.2805, 0.2610, 0.2654, 0.2497, 0.2406, 0.3008, 0.2444,\n",
-       "         0.2785, 0.2054, 0.2588, 0.2461, 0.2662, 0.2472, 0.2347, 0.2263, 0.2728,\n",
-       "         0.2666, 0.3150, 0.3053, 0.2641, 0.2255, 0.2427, 0.2786, 0.2484, 0.2381,\n",
-       "         0.2602, 0.2766, 0.2940, 0.2216, 0.2485, 0.2555, 0.2402, 0.2495, 0.2686,\n",
-       "         0.2738, 0.2197, 0.2465, 0.3025, 0.2009, 0.2519, 0.2477, 0.2205, 0.2375,\n",
-       "         0.2485, 0.2456, 0.2581, 0.2716, 0.2492, 0.2673, 0.2505, 0.2070, 0.2255,\n",
-       "         0.2403, 0.2614, 0.1890, 0.2344, 0.2817, 0.2847, 0.2300, 0.2115, 0.2651,\n",
-       "         0.2947, 0.2558, 0.2431, 0.2591, 0.2463, 0.2474, 0.2886, 0.2208, 0.2409,\n",
-       "         0.2356, 0.2429, 0.3004, 0.2641, 0.2369, 0.2158, 0.2155, 0.2594, 0.2690,\n",
-       "         0.2824, 0.2662, 0.2545, 0.2532, 0.2362, 0.2402, 0.2686, 0.2766, 0.2334,\n",
-       "         0.2521, 0.2307, 0.2721, 0.2376, 0.2416, 0.2478, 0.2313, 0.2439, 0.2779,\n",
-       "         0.2381, 0.2715, 0.2496, 0.2571, 0.2368, 0.2381, 0.2498, 0.2110, 0.2586,\n",
-       "         0.2342, 0.2931, 0.2262, 0.2868, 0.2506, 0.2723, 0.2846, 0.2571, 0.2856,\n",
-       "         0.2367, 0.2788, 0.2599, 0.2766, 0.2274, 0.2537, 0.2464, 0.2301, 0.2603,\n",
-       "         0.2384, 0.2501, 0.3040, 0.2551, 0.2683, 0.2412, 0.2353, 0.2567, 0.2580,\n",
-       "         0.2349, 0.2574, 0.2628, 0.2591, 0.2176, 0.2259, 0.2602, 0.2481, 0.2332,\n",
-       "         0.2104, 0.2466, 0.2913, 0.2340, 0.2412, 0.2927, 0.2619, 0.2350, 0.2717,\n",
-       "         0.2759, 0.2276, 0.2409, 0.2856, 0.2230, 0.2918, 0.2078, 0.2252, 0.2236,\n",
-       "         0.2537, 0.2774, 0.2275, 0.2337, 0.2909, 0.2779, 0.1481, 0.2068, 0.2405,\n",
-       "         0.2426, 0.2442, 0.2626, 0.2675, 0.2650, 0.3415, 0.2789, 0.2261, 0.2434,\n",
-       "         0.2731, 0.2603, 0.2736, 0.2416, 0.2977, 0.2926, 0.2816, 0.2222, 0.2473,\n",
-       "         0.3014, 0.2847, 0.2602, 0.2608, 0.2519, 0.3238, 0.2296, 0.2187, 0.1932,\n",
-       "         0.2917, 0.2533, 0.2443, 0.2766, 0.2454, 0.2534, 0.2412, 0.2828, 0.2790,\n",
-       "         0.2424, 0.2601, 0.2814, 0.2480, 0.2702, 0.2375, 0.2740, 0.2797, 0.2800,\n",
-       "         0.2593, 0.2709, 0.2463, 0.2638, 0.2390, 0.2142, 0.2235, 0.2724, 0.2939,\n",
-       "         0.3002, 0.2260, 0.2652, 0.3286, 0.2950, 0.2817, 0.2653, 0.3121, 0.2404,\n",
-       "         0.2506, 0.2331, 0.2279, 0.2693, 0.2753, 0.2141, 0.3458, 0.2505, 0.2854,\n",
-       "         0.2331, 0.2845, 0.2646, 0.2403, 0.2037, 0.2427, 0.3224, 0.2571, 0.2382,\n",
-       "         0.2253, 0.2295, 0.2451, 0.2163, 0.2505, 0.2987, 0.2266, 0.2323, 0.2806,\n",
-       "         0.2658, 0.2477, 0.2412, 0.2307, 0.2249, 0.3043, 0.2487, 0.2253, 0.2548,\n",
-       "         0.2206, 0.2395, 0.2813, 0.2179, 0.2854, 0.2378, 0.2664, 0.3018, 0.2694,\n",
-       "         0.2427, 0.2532, 0.2585, 0.3147, 0.2598, 0.2471, 0.2688, 0.2889, 0.2538,\n",
-       "         0.2595, 0.2734, 0.2673, 0.2403, 0.3142, 0.2589, 0.2705, 0.2514, 0.2294,\n",
-       "         0.2557, 0.2487, 0.2694, 0.2611, 0.2629, 0.2591, 0.2782, 0.2810, 0.2546,\n",
-       "         0.1172, 0.2513, 0.1908, 0.2263, 0.2570, 0.2705, 0.2609, 0.2685, 0.2549,\n",
-       "         0.2489, 0.2406, 0.2734, 0.2481, 0.2510, 0.2384, 0.2459, 0.2439, 0.2462,\n",
-       "         0.2640, 0.2323, 0.2985, 0.2933, 0.2067, 0.2403, 0.2287, 0.2459, 0.2776,\n",
-       "         0.2682, 0.2606, 0.2341, 0.2857, 0.2636, 0.2489, 0.2749, 0.2506, 0.2884,\n",
-       "         0.2492, 0.2417, 0.2635, 0.2758, 0.2573, 0.2374, 0.2485, 0.2653, 0.2887,\n",
-       "         0.2938, 0.2663, 0.2223, 0.2963, 0.2786, 0.2883, 0.2058, 0.2555, 0.3074,\n",
-       "         0.3057, 0.2886, 0.2519, 0.2472, 0.2269, 0.2430, 0.2544, 0.2562, 0.2848,\n",
-       "         0.2054, 0.2346, 0.2826, 0.2997, 0.2203, 0.2564, 0.2707, 0.2842, 0.2663,\n",
-       "         0.2442, 0.2517, 0.2992, 0.2758, 0.2722, 0.2958, 0.2487, 0.1751, 0.2272,\n",
-       "         0.2460, 0.2689, 0.2658, 0.2779, 0.1979, 0.2182, 0.2675, 0.2548, 0.2229,\n",
-       "         0.2479, 0.2764, 0.2804, 0.2541, 0.2669, 0.1158, 0.2579, 0.2207, 0.2628,\n",
-       "         0.2699, 0.2385, 0.2185, 0.2779, 0.2703, 0.2478, 0.2517, 0.2384, 0.2356,\n",
-       "         0.2907, 0.2302, 0.2410, 0.3131, 0.2569, 0.2661, 0.2257, 0.2205, 0.2391,\n",
-       "         0.2256, 0.2284, 0.2540, 0.2734, 0.2615, 0.2865, 0.2145, 0.2580, 0.2284,\n",
-       "         0.2179, 0.2541, 0.2288, 0.2840, 0.2699, 0.2435, 0.2356, 0.2466],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.2734, -0.1986, -0.1880, -0.2294, -0.1980, -0.2167, -0.0983, -0.2207,\n",
-       "         -0.1532, -0.2116, -0.2011, -0.2116, -0.1757, -0.2198, -0.1439, -0.1899,\n",
-       "         -0.0926, -0.1959, -0.1550, -0.2022, -0.2275, -0.2109, -0.0056, -0.1967,\n",
-       "         -0.2722, -0.2127, -0.2459, -0.2393, -0.1595, -0.2519, -0.1308, -0.2025,\n",
-       "         -0.2225, -0.2022, -0.1933, -0.2567, -0.1960, -0.2223, -0.1729, -0.1554,\n",
-       "         -0.1823, -0.1783, -0.2205, -0.1706, -0.1595, -0.1829, -0.0862, -0.1771,\n",
-       "         -0.1745, -0.1963, -0.2386, -0.2477, -0.1738, -0.2017, -0.1712, -0.2192,\n",
-       "         -0.1355, -0.0965, -0.2512, -0.2336, -0.2016, -0.1384, -0.1540, -0.1127,\n",
-       "         -0.2239, -0.1197, -0.1644, -0.1826, -0.2200, -0.1564, -0.1861, -0.2440,\n",
-       "         -0.1581, -0.2662, -0.2398, -0.2542, -0.1842, -0.1242, -0.2608, -0.2811,\n",
-       "         -0.1068, -0.1949, -0.0980, -0.1615, -0.2830, -0.2626, -0.2368, -0.1146,\n",
-       "         -0.1614, -0.2091, -0.2073, -0.2272, -0.2088, -0.1695, -0.1774, -0.1782,\n",
-       "         -0.2252, -0.2389, -0.1820, -0.2433, -0.2993, -0.2191, -0.1317, -0.1624,\n",
-       "         -0.2020, -0.2155, -0.2023, -0.2737, -0.2933, -0.1733, -0.2397, -0.1726,\n",
-       "         -0.0647, -0.2118, -0.1374, -0.1294, -0.1566, -0.2410, -0.1826, -0.2672,\n",
-       "         -0.1591, -0.2938, -0.1963, -0.1904, -0.1088, -0.1322, -0.2453, -0.2191,\n",
-       "         -0.0558, -0.1951, -0.2556, -0.2529, -0.1719, -0.1063, -0.2073, -0.2258,\n",
-       "         -0.2236, -0.1961, -0.1949, -0.1837, -0.1867, -0.1747, -0.2189, -0.1336,\n",
-       "         -0.1134, -0.1558, -0.2456, -0.2375, -0.1478, -0.1744, -0.1317, -0.1417,\n",
-       "         -0.2163, -0.2096, -0.1210, -0.1856, -0.1506, -0.2328, -0.1475, -0.2074,\n",
-       "         -0.1395, -0.1276, -0.1714, -0.1545, -0.2049, -0.2060, -0.1768, -0.1228,\n",
-       "         -0.1258, -0.1826, -0.1884, -0.0969, -0.2319, -0.1912, -0.1811, -0.1975,\n",
-       "         -0.1401, -0.2469, -0.1391, -0.2421, -0.2077, -0.2770, -0.1594, -0.2577,\n",
-       "         -0.2094, -0.1874, -0.2373, -0.2023, -0.1783, -0.1453, -0.1457, -0.1473,\n",
-       "         -0.2324, -0.2048, -0.1804, -0.2548, -0.1799, -0.2506, -0.1717, -0.1834,\n",
-       "         -0.2535, -0.2251, -0.3271, -0.1511, -0.1087, -0.2030, -0.2035, -0.1496,\n",
-       "         -0.1815, -0.1169, -0.2243, -0.1595, -0.1536, -0.1964, -0.1658, -0.1506,\n",
-       "         -0.1210, -0.2006, -0.2902, -0.1930, -0.1980, -0.1666, -0.1896, -0.1560,\n",
-       "         -0.1922, -0.1541, -0.1555, -0.1237, -0.1576, -0.1658, -0.3104, -0.1414,\n",
-       "         -0.1470, -0.1347, -0.1750, -0.2650, -0.1345, -0.1884, -0.1658, -0.1610,\n",
-       "         -0.0066, -0.1570, -0.0871, -0.1983, -0.1253, -0.1894, -0.2581, -0.1462,\n",
-       "         -0.3704, -0.2513, -0.1036, -0.2044, -0.2317, -0.1576, -0.1726, -0.1555,\n",
-       "         -0.2305, -0.2172, -0.2098, -0.1222, -0.2801, -0.3123, -0.2857, -0.2264,\n",
-       "         -0.1825, -0.2397, -0.2806, -0.1500, -0.1788, -0.1405, -0.2329, -0.1474,\n",
-       "         -0.1982, -0.2491, -0.2403, -0.1946, -0.2068, -0.1832, -0.2148, -0.2453,\n",
-       "         -0.1760, -0.2044, -0.1949, -0.2900, -0.1960, -0.2686, -0.2155, -0.2646,\n",
-       "         -0.2078, -0.2263, -0.1930, -0.1970, -0.1559, -0.1026, -0.2148, -0.2434,\n",
-       "         -0.2453, -0.2677, -0.1394, -0.1166, -0.2702, -0.2339, -0.1122, -0.2256,\n",
-       "         -0.2387, -0.1385, -0.2139, -0.1337, -0.1512, -0.2402, -0.1675, -0.1400,\n",
-       "         -0.3665, -0.1959, -0.3213, -0.1615, -0.1985, -0.1735, -0.2179, -0.1331,\n",
-       "         -0.1886, -0.3017, -0.1959, -0.1377, -0.1810, -0.1697, -0.1395, -0.1740,\n",
-       "         -0.2365, -0.2578, -0.1476, -0.1278, -0.2444, -0.2637, -0.2106, -0.2126,\n",
-       "         -0.2116, -0.1748, -0.3168, -0.1930, -0.2098, -0.1702, -0.1398, -0.1317,\n",
-       "         -0.2261, -0.1294, -0.2798, -0.1665, -0.2596, -0.2925, -0.1700, -0.2181,\n",
-       "         -0.1863, -0.2264, -0.2787, -0.1831, -0.1708, -0.2075, -0.2370, -0.1846,\n",
-       "         -0.1630, -0.2679, -0.2054, -0.1032, -0.2307, -0.2232, -0.1452, -0.2140,\n",
-       "         -0.1731, -0.2123, -0.1978, -0.2026, -0.1549, -0.2722, -0.1870, -0.2320,\n",
-       "         -0.1337, -0.2301,  0.1091, -0.2244, -0.1129, -0.1682, -0.2850, -0.2691,\n",
-       "         -0.2429, -0.2699, -0.2174, -0.2277, -0.1964, -0.2254, -0.1508, -0.1465,\n",
-       "         -0.1506, -0.1600, -0.1927, -0.2062, -0.2094, -0.1450, -0.2269, -0.1926,\n",
-       "         -0.1456, -0.1823, -0.1396, -0.2145, -0.1841, -0.1961, -0.1562, -0.2250,\n",
-       "         -0.2656, -0.1702, -0.1868, -0.2404, -0.1680, -0.1812, -0.1837, -0.1456,\n",
-       "         -0.2119, -0.2388, -0.2230, -0.2418, -0.2087, -0.2026, -0.2268, -0.1312,\n",
-       "         -0.1941, -0.1576, -0.2205, -0.2294, -0.3825, -0.0671, -0.1797, -0.2590,\n",
-       "         -0.1755, -0.2358, -0.1729, -0.1803, -0.1822, -0.1767, -0.2465, -0.2227,\n",
-       "         -0.2355, -0.1166, -0.1520, -0.3060, -0.1931, -0.1146, -0.2082, -0.2291,\n",
-       "         -0.1930, -0.2068, -0.1827, -0.1491, -0.2853, -0.2367, -0.1875, -0.2341,\n",
-       "         -0.1804, -0.0081, -0.1144, -0.1722, -0.2562, -0.2520, -0.2458, -0.1335,\n",
-       "         -0.1445, -0.2143, -0.2165, -0.1664, -0.2536, -0.2867, -0.2017, -0.2018,\n",
-       "         -0.2323,  0.1057, -0.2493, -0.1666, -0.2576, -0.2183, -0.1734, -0.1426,\n",
-       "         -0.2175, -0.2897, -0.1523, -0.1816, -0.1263, -0.1774, -0.2838, -0.1489,\n",
-       "         -0.2178, -0.3439, -0.1608, -0.2525, -0.1790, -0.0934, -0.1735, -0.1324,\n",
-       "         -0.1731, -0.1641, -0.2202, -0.2699, -0.1899, -0.2122, -0.2272, -0.1716,\n",
-       "         -0.1288, -0.2347, -0.1875, -0.1865, -0.2519, -0.2454, -0.1825, -0.1808],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 1.4110e-02,  7.9640e-03,  5.0586e-03],\n",
-       "           [-3.5183e-03, -1.1302e-02, -1.3451e-02],\n",
-       "           [-1.0626e-02, -1.3320e-02, -1.8028e-02]],\n",
-       " \n",
-       "          [[ 1.3137e-02, -6.0093e-03, -1.0737e-03],\n",
-       "           [ 1.0550e-02,  1.1335e-03, -1.5361e-03],\n",
-       "           [-1.8859e-02, -2.7555e-02, -3.6983e-02]],\n",
-       " \n",
-       "          [[-3.3377e-03, -2.0416e-02, -1.8446e-02],\n",
-       "           [-1.1214e-02, -2.0475e-02, -1.6992e-02],\n",
-       "           [-2.0632e-02, -2.1415e-02, -1.4489e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 7.7323e-03,  5.3579e-03,  1.1211e-02],\n",
-       "           [-8.3707e-03, -7.4740e-03, -5.6970e-03],\n",
-       "           [ 1.1818e-02, -2.6374e-05,  1.5254e-02]],\n",
-       " \n",
-       "          [[ 1.8495e-02,  1.7512e-02,  2.6989e-02],\n",
-       "           [ 2.2766e-02,  1.2697e-02,  2.4580e-02],\n",
-       "           [ 3.6980e-03,  1.4935e-03,  1.6474e-02]],\n",
-       " \n",
-       "          [[-6.4330e-04,  1.4941e-04, -5.9528e-05],\n",
-       "           [ 5.6156e-04, -1.0112e-02, -3.2395e-03],\n",
-       "           [-1.1713e-02, -1.4559e-02, -2.9060e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.4913e-03, -4.9731e-04,  1.6523e-03],\n",
-       "           [ 2.0359e-03,  2.5829e-04, -4.9600e-03],\n",
-       "           [-1.9769e-03,  9.2138e-03,  2.4978e-03]],\n",
-       " \n",
-       "          [[ 1.6592e-03,  3.7073e-03,  7.2997e-03],\n",
-       "           [-1.1925e-02, -1.6093e-02, -3.7419e-03],\n",
-       "           [-2.6836e-02, -4.0111e-02, -3.5321e-02]],\n",
-       " \n",
-       "          [[-3.0430e-03, -3.0870e-03, -5.0979e-03],\n",
-       "           [-3.8921e-02, -3.8858e-02, -4.4715e-02],\n",
-       "           [-2.5277e-02, -3.3549e-02, -2.7383e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.6236e-02,  2.0113e-03,  1.1520e-02],\n",
-       "           [-9.4082e-03, -8.5980e-03,  6.9895e-03],\n",
-       "           [-1.4202e-02, -2.4120e-02, -9.8814e-03]],\n",
-       " \n",
-       "          [[-9.7806e-03, -1.4190e-02, -5.8684e-03],\n",
-       "           [-9.7006e-03,  8.8742e-03, -7.9713e-03],\n",
-       "           [-2.1772e-03,  5.6326e-03,  5.8199e-03]],\n",
-       " \n",
-       "          [[ 1.7721e-02,  1.8572e-02,  1.1253e-02],\n",
-       "           [ 8.5851e-03,  3.8022e-02,  2.7984e-02],\n",
-       "           [ 2.5480e-03,  2.5757e-02,  3.6054e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-9.5974e-03, -7.6772e-03, -1.7638e-03],\n",
-       "           [-1.7315e-02, -2.1178e-02, -1.6767e-02],\n",
-       "           [-1.2623e-02, -2.9873e-02, -1.5783e-02]],\n",
-       " \n",
-       "          [[ 1.0677e-02,  1.5297e-02,  1.7072e-02],\n",
-       "           [ 1.4614e-02,  1.2394e-02,  1.8239e-02],\n",
-       "           [-4.8800e-03, -1.0698e-02, -6.3845e-03]],\n",
-       " \n",
-       "          [[-1.2130e-02, -8.3466e-04, -1.8237e-02],\n",
-       "           [-1.1967e-02,  3.0960e-03, -7.0793e-03],\n",
-       "           [-2.7758e-03,  4.2042e-04,  7.7025e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.2617e-02, -1.0013e-02, -1.3439e-02],\n",
-       "           [ 5.4978e-03, -5.9879e-03, -1.7802e-03],\n",
-       "           [ 9.3989e-04,  4.9900e-03, -1.3538e-02]],\n",
-       " \n",
-       "          [[ 9.4860e-03,  8.7861e-03,  3.6195e-03],\n",
-       "           [ 1.4494e-02,  2.8750e-02,  1.5275e-02],\n",
-       "           [ 4.2211e-03,  9.8786e-03,  9.0280e-03]],\n",
-       " \n",
-       "          [[-1.7676e-03, -1.4504e-02, -1.0101e-02],\n",
-       "           [-1.3060e-02, -2.0568e-02, -2.3557e-02],\n",
-       "           [-1.6348e-02,  7.8955e-04,  7.6869e-03]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 2.7926e-03,  4.5525e-03, -7.1865e-04],\n",
-       "           [ 9.2693e-03,  5.9484e-03, -1.8687e-03],\n",
-       "           [-8.4860e-03,  9.8893e-04, -1.0855e-02]],\n",
-       " \n",
-       "          [[ 3.5104e-02,  3.2944e-02,  3.5649e-02],\n",
-       "           [ 2.6063e-02,  2.5998e-02,  2.5129e-02],\n",
-       "           [ 1.3269e-02,  1.0781e-02,  9.8807e-03]],\n",
-       " \n",
-       "          [[ 1.4290e-02,  1.3566e-02,  3.7804e-03],\n",
-       "           [-1.7589e-02, -3.5515e-03, -5.6566e-03],\n",
-       "           [-9.6105e-04,  1.6512e-02, -2.8784e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.6703e-02,  9.0411e-03,  9.3036e-03],\n",
-       "           [ 1.6139e-02,  9.8411e-03,  2.0556e-02],\n",
-       "           [ 4.1374e-03,  4.2981e-03, -3.4907e-03]],\n",
-       " \n",
-       "          [[-1.2853e-02, -8.4555e-03, -1.5208e-02],\n",
-       "           [-1.3714e-02, -9.1006e-03, -1.2532e-02],\n",
-       "           [-7.4832e-03, -1.5091e-02, -1.7320e-02]],\n",
-       " \n",
-       "          [[-6.0303e-03, -7.2753e-03, -1.9063e-02],\n",
-       "           [ 1.2305e-02,  1.9688e-02, -1.3534e-02],\n",
-       "           [ 1.0987e-02,  1.9910e-02,  1.5589e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 8.2481e-03,  1.4494e-02,  1.9291e-02],\n",
-       "           [-1.5061e-02, -3.5962e-03, -1.8515e-02],\n",
-       "           [-3.0408e-02, -2.7109e-02, -3.0712e-02]],\n",
-       " \n",
-       "          [[-1.9704e-03,  9.6652e-03,  7.0560e-03],\n",
-       "           [-6.0572e-03,  1.5232e-02,  1.6445e-02],\n",
-       "           [ 5.1932e-03,  1.2473e-02, -2.1994e-03]],\n",
-       " \n",
-       "          [[ 8.5485e-03, -8.1445e-04, -8.4493e-04],\n",
-       "           [-7.6289e-04, -2.3580e-02, -1.1374e-02],\n",
-       "           [ 2.2989e-03, -1.3075e-02,  1.8526e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-7.3668e-03, -2.7272e-02, -2.5626e-02],\n",
-       "           [-3.0837e-02, -3.7469e-02, -4.5970e-02],\n",
-       "           [-3.0294e-02, -4.8235e-02, -3.5411e-02]],\n",
-       " \n",
-       "          [[-1.1492e-02, -2.9376e-03, -2.7226e-03],\n",
-       "           [ 9.4872e-03,  2.1659e-02,  1.6838e-02],\n",
-       "           [-7.8715e-03,  1.7357e-02,  9.3706e-04]],\n",
-       " \n",
-       "          [[-4.7322e-03,  7.9475e-03,  1.1009e-02],\n",
-       "           [ 3.4936e-03,  1.4975e-02,  1.1908e-02],\n",
-       "           [-3.6993e-03, -9.5125e-03, -5.5578e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.2203e-03, -6.3163e-03, -1.1490e-02],\n",
-       "           [ 1.7454e-02, -2.9090e-03,  6.6832e-03],\n",
-       "           [ 1.4506e-02,  4.4097e-03,  1.1017e-02]],\n",
-       " \n",
-       "          [[-3.1321e-03, -1.1267e-03,  5.1866e-03],\n",
-       "           [ 2.1737e-03, -8.1456e-03,  8.8426e-03],\n",
-       "           [-1.1865e-02, -1.2617e-02,  6.9173e-03]],\n",
-       " \n",
-       "          [[ 5.2445e-03,  1.4236e-03, -4.7172e-03],\n",
-       "           [ 1.4212e-02,  1.6139e-02,  8.6425e-03],\n",
-       "           [ 1.1626e-02,  1.5302e-02,  2.0117e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 9.2146e-03,  1.7742e-02,  6.4551e-03],\n",
-       "           [-3.0385e-03, -8.9094e-03, -1.1750e-03],\n",
-       "           [-1.0877e-04, -3.2898e-03,  7.4582e-03]],\n",
-       " \n",
-       "          [[-7.5833e-03, -8.7543e-03,  1.6448e-03],\n",
-       "           [-1.6821e-02, -1.8386e-02, -1.4012e-02],\n",
-       "           [-1.1409e-02, -1.3753e-02, -2.7126e-02]],\n",
-       " \n",
-       "          [[ 2.0488e-02,  2.6724e-02,  1.8305e-02],\n",
-       "           [ 1.0774e-02,  7.8425e-03,  1.5748e-02],\n",
-       "           [ 1.1546e-02,  7.2906e-03,  1.2676e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.6983, 0.6593, 0.7881, 0.6917, 0.7346, 0.7169, 0.8098, 0.7297, 0.7868,\n",
-       "         0.7535, 0.7224, 0.7571, 0.7041, 0.7507, 0.6815, 0.8186, 0.7343, 0.7612,\n",
-       "         0.7284, 0.7862, 0.7197, 0.7322, 0.7641, 0.7311, 0.7149, 0.8317, 0.7003,\n",
-       "         0.8048, 0.7684, 0.7431, 0.6397, 0.6989, 0.7742, 0.7323, 0.6874, 0.7247,\n",
-       "         0.6843, 0.7074, 0.8137, 0.7467, 0.8026, 0.7034, 0.7659, 0.7530, 0.7301,\n",
-       "         0.7680, 0.8403, 0.7847, 0.7223, 0.7469, 0.6992, 0.7784, 0.8120, 0.7255,\n",
-       "         0.7393, 0.7106, 0.7634, 0.6399, 0.7864, 0.7255, 0.7326, 0.7104, 0.6911,\n",
-       "         0.6399, 0.7519, 0.7987, 0.7388, 0.7201, 0.7469, 0.7471, 0.7507, 0.7639,\n",
-       "         0.6983, 0.7517, 0.7241, 0.7758, 0.7820, 0.7352, 0.4884, 0.6838, 0.7389,\n",
-       "         0.7288, 0.7117, 0.6754, 0.7240, 0.7375, 0.7681, 0.7356, 0.7020, 0.7190,\n",
-       "         0.8541, 0.7063, 0.6326, 0.7083, 0.7387, 0.8159, 0.8015, 0.7160, 0.6761,\n",
-       "         0.7531, 0.7440, 0.8247, 0.7566, 0.7565, 0.7497, 0.7598, 0.7642, 0.7204,\n",
-       "         0.7132, 0.7017, 0.7413, 0.7276, 0.6968, 0.7431, 0.7531, 0.7725, 0.7548,\n",
-       "         0.6956, 0.7696, 0.6494, 0.7292, 0.7289, 0.7473, 0.7091, 0.6933, 0.7630,\n",
-       "         0.6761, 0.6943, 0.7733, 0.8075, 0.7319, 0.6897, 0.7527, 0.7187, 0.6995,\n",
-       "         0.8271, 0.6661, 0.7704, 0.7158, 0.7903, 0.7200, 0.6490, 0.7647, 0.7262,\n",
-       "         0.7560, 0.7247, 0.7368, 0.7622, 0.6841, 0.7417, 0.7167, 0.7666, 0.7211,\n",
-       "         0.6818, 0.7037, 0.7633, 0.7277, 0.7713, 0.7106, 0.7520, 0.6699, 0.7166,\n",
-       "         0.7647, 0.7638, 0.9556, 0.6813, 0.6857, 0.6843, 0.6747, 0.7668, 0.8068,\n",
-       "         0.7299, 0.7283, 0.7120, 0.6837, 0.6407, 0.7378, 0.7233, 0.7080, 0.7752,\n",
-       "         0.7690, 0.7255, 0.7263, 0.7803, 0.7596, 0.7524, 0.6904, 0.6923, 0.7118,\n",
-       "         0.8146, 0.8007, 0.7568, 0.7487, 0.8094, 0.7587, 0.7167, 0.7373, 0.7117,\n",
-       "         0.8274, 0.7427, 0.7017, 0.7972, 0.7322, 0.6902, 0.7087, 0.6956, 0.6884,\n",
-       "         0.7433, 0.7684, 0.6943, 0.7583, 0.7144, 0.7619, 0.7816, 0.7307, 0.7708,\n",
-       "         0.7518, 0.6572, 0.7091, 0.7812, 0.7491, 0.5799, 0.6965, 0.7129, 0.7679,\n",
-       "         0.7198, 0.7390, 0.7141, 0.7523, 0.7294, 0.7371, 0.6765, 0.7587, 0.7165,\n",
-       "         0.6980, 0.7366, 0.6599, 0.8961, 0.6920, 0.6941, 0.7200, 0.6974, 0.7656,\n",
-       "         0.7744, 0.7289, 0.7618, 0.8261, 0.7148, 0.7069, 0.7574, 0.7326, 0.7289,\n",
-       "         0.7005, 0.7352, 0.7540, 0.7172, 0.7499, 0.7147, 0.7470, 0.7613, 0.7168,\n",
-       "         0.7993, 0.7026, 0.7380, 0.6019, 0.5970, 0.7184, 0.7012, 0.7563, 0.7547,\n",
-       "         0.7194, 0.7186, 0.7630, 0.8379, 0.7153, 0.7012, 0.7423, 0.7044, 0.7349,\n",
-       "         0.7148, 0.7565, 0.7741, 0.7203, 0.6784, 0.6550, 0.7572, 0.7850, 0.7167,\n",
-       "         0.6989, 0.7471, 0.7823, 0.7266, 0.8464, 0.7069, 0.7401, 0.8562, 0.8054,\n",
-       "         0.7370, 0.7745, 0.7535, 0.7491, 0.7572, 0.7476, 0.7199, 0.7223, 0.7524,\n",
-       "         0.7747, 0.6908, 0.7477, 0.7302, 0.7029, 0.7298, 0.7771, 0.7171, 0.7854,\n",
-       "         0.8583, 0.7116, 0.7198, 0.7200, 0.7305, 0.7875, 0.8335, 0.7506, 0.7723,\n",
-       "         0.6965, 0.7617, 0.6887, 0.7364, 0.6820, 0.7806, 0.7701, 0.7330, 0.6460,\n",
-       "         0.7445, 0.6870, 0.7739, 0.7757, 0.7674, 0.7518, 0.7058, 0.7771, 0.7051,\n",
-       "         0.7202, 0.8051, 0.7655, 0.6963, 0.6804, 0.8003, 0.9323, 0.7287, 0.7052,\n",
-       "         0.7392, 0.7020, 0.7426, 0.7520, 0.7130, 0.7309, 0.7298, 0.7598, 0.7562,\n",
-       "         0.7981, 0.8229, 0.7528, 0.7177, 0.7732, 0.7832, 0.7418, 0.7438, 0.7606,\n",
-       "         0.8304, 0.7887, 0.7694, 0.7230, 0.7175, 0.7282, 0.6889, 0.7078, 0.7054,\n",
-       "         0.7336, 0.7317, 0.7212, 0.6876, 0.7328, 0.7267, 0.7478, 0.7132, 0.7386,\n",
-       "         0.7318, 0.7323, 0.7996, 0.6636, 0.7341, 0.8042, 0.7502, 0.7325, 0.8028,\n",
-       "         0.8138, 0.7277, 0.8738, 0.7458, 0.7716, 0.7321, 0.7108, 0.7674, 0.6883,\n",
-       "         0.7202, 0.8080, 0.7304, 0.7156, 0.7213, 0.7355, 0.7238, 0.7171, 0.7291,\n",
-       "         0.7237, 0.8732, 0.7196, 0.7915, 0.8256, 0.7364, 0.8202, 0.7806, 0.7709,\n",
-       "         0.8122, 0.7480, 0.7805, 0.6969, 0.7778, 0.7884, 0.7224, 0.7133, 0.7106,\n",
-       "         0.7031, 0.7600, 0.7898, 0.7108, 0.7202, 0.7310, 0.8112, 0.6624, 0.7418,\n",
-       "         0.7200, 0.7122, 0.8819, 0.7213, 0.7079, 0.7456, 0.7407, 0.7398, 0.6874,\n",
-       "         0.7521, 0.7501, 0.6961, 0.7275, 0.7685, 0.7344, 0.7249, 0.6834, 0.7281,\n",
-       "         0.7188, 0.7983, 0.8003, 0.7312, 0.7244, 0.7325, 0.7323, 0.6764, 0.8291,\n",
-       "         0.7251, 0.7195, 0.7065, 0.6923, 0.8042, 0.7633, 0.6620, 0.7864, 0.7706,\n",
-       "         0.7250, 0.7869, 0.7384, 0.7111, 0.6476, 0.7606, 0.7196, 0.8234, 0.7349,\n",
-       "         0.7453, 0.7191, 0.8577, 0.7107, 0.6807, 0.7364, 0.7357, 0.7865, 0.8347,\n",
-       "         0.6753, 0.6866, 0.8406, 0.7170, 0.7954, 0.6921, 0.7249, 0.7197, 0.7169,\n",
-       "         0.7235, 0.7089, 0.7990, 0.7415, 0.7248, 0.7310, 0.7320, 0.7199],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.0746, -0.1249, -0.1581, -0.0763, -0.0770, -0.0951, -0.1406, -0.0324,\n",
-       "         -0.1334, -0.0702, -0.0784, -0.1666, -0.0131, -0.1315, -0.1021, -0.1130,\n",
-       "          0.0048, -0.1203, -0.0950, -0.1735,  0.0663, -0.0821, -0.0353, -0.0819,\n",
-       "         -0.1141, -0.1661, -0.0794, -0.1360, -0.1013, -0.1492,  0.0341, -0.0986,\n",
-       "         -0.1157, -0.0875, -0.1145, -0.0401, -0.0099, -0.1313, -0.1183, -0.1569,\n",
-       "         -0.1525, -0.1086, -0.1493, -0.0954, -0.1428, -0.0994, -0.1944, -0.0182,\n",
-       "         -0.0763, -0.0941, -0.0968, -0.0352, -0.1753, -0.0701, -0.1315, -0.1441,\n",
-       "         -0.1017, -0.1790, -0.1882, -0.1211, -0.0910, -0.0641, -0.0600, -0.0956,\n",
-       "         -0.0697, -0.1949, -0.0652, -0.0779, -0.0915, -0.0924, -0.0959, -0.0679,\n",
-       "         -0.0985, -0.1308, -0.0841, -0.0260, -0.1210,  0.0138,  0.2261, -0.0901,\n",
-       "         -0.0638, -0.0957, -0.0456, -0.0872, -0.1177, -0.1745, -0.0431, -0.1083,\n",
-       "         -0.0142, -0.1748, -0.0327, -0.1127,  0.0066, -0.0477, -0.0981, -0.1665,\n",
-       "         -0.0711, -0.0584, -0.0459, -0.1330, -0.0952, -0.1632, -0.1898, -0.1315,\n",
-       "         -0.1107, -0.1625, -0.1117, -0.0894, -0.1421, -0.1346, -0.0743, -0.1761,\n",
-       "         -0.0205, -0.0312, -0.1321, -0.1059, -0.1236,  0.0791, -0.0579, -0.0283,\n",
-       "         -0.1148, -0.0618, -0.1328, -0.0641, -0.0984, -0.0574, -0.0889, -0.0061,\n",
-       "         -0.1529, -0.2062, -0.0970, -0.0640, -0.1053, -0.1218, -0.0922, -0.1553,\n",
-       "          0.0243, -0.0662, -0.0492, -0.1848, -0.0892, -0.0131, -0.1471, -0.1279,\n",
-       "         -0.1220, -0.1026, -0.0381, -0.1451, -0.0368, -0.1041, -0.0771, -0.1270,\n",
-       "         -0.1105,  0.0447, -0.1324, -0.1146, -0.0532, -0.0785, -0.0301, -0.0254,\n",
-       "         -0.0057, -0.0785, -0.1129, -0.1357, -0.1558, -0.1138, -0.0803, -0.0783,\n",
-       "         -0.0669, -0.1002, -0.1857, -0.1158, -0.0982, -0.1064, -0.0636, -0.0236,\n",
-       "         -0.0906, -0.1044, -0.1171, -0.1515, -0.1118, -0.1432, -0.1280, -0.1417,\n",
-       "         -0.1362, -0.0757, -0.1253, -0.0938, -0.1364, -0.1078, -0.0401, -0.1653,\n",
-       "         -0.1358, -0.1316, -0.1320, -0.0482, -0.0706, -0.0890, -0.1296, -0.0873,\n",
-       "         -0.0890, -0.1501, -0.1370, -0.0898, -0.1072, -0.0275, -0.0044, -0.0426,\n",
-       "         -0.1236, -0.0466, -0.1470, -0.1117, -0.1572, -0.0684, -0.0878, -0.1064,\n",
-       "         -0.1842, -0.1188, -0.0772, -0.1162, -0.1455,  0.1304, -0.1273, -0.1089,\n",
-       "         -0.1299, -0.1398, -0.0736, -0.1178, -0.0381, -0.1164, -0.1241, -0.0711,\n",
-       "         -0.1103, -0.0545, -0.0302, -0.0814, -0.0854, -0.1701, -0.0676, -0.0432,\n",
-       "         -0.0732, -0.0054, -0.1058, -0.0837, -0.1216, -0.1122, -0.1916, -0.0690,\n",
-       "         -0.0815, -0.0895, -0.0874, -0.1336, -0.0698, -0.0946, -0.0862, -0.0641,\n",
-       "         -0.1471, -0.1252, -0.1359, -0.0896, -0.0960, -0.1739, -0.1178, -0.0499,\n",
-       "          0.0058,  0.1777, -0.0859, -0.0604, -0.0778, -0.1612, -0.0578,  0.0105,\n",
-       "         -0.0809, -0.0510, -0.1499, -0.1185, -0.0947, -0.0797, -0.1398, -0.0963,\n",
-       "         -0.0461, -0.1857, -0.0876, -0.0642,  0.2263, -0.1416,  0.1731, -0.0672,\n",
-       "         -0.0961, -0.1842, -0.1553, -0.0556, -0.0590, -0.0616, -0.1188, -0.1709,\n",
-       "         -0.1434, -0.0966,  0.2476, -0.1056, -0.1430, -0.1294, -0.0701, -0.1234,\n",
-       "         -0.0666, -0.0961, -0.1301, -0.1044, -0.1442, -0.0197, -0.0737, -0.1153,\n",
-       "         -0.1270, -0.1448, -0.1786, -0.1814, -0.0741, -0.1119, -0.0764, -0.0605,\n",
-       "         -0.0839, -0.1996, -0.0933,  0.1616, -0.0515, -0.1245, -0.0286, -0.0910,\n",
-       "         -0.0901, -0.0276, -0.0470, -0.0769,  0.0207, -0.1004, -0.0904, -0.1074,\n",
-       "         -0.1872, -0.1303, -0.1112, -0.1033, -0.1274, -0.0733, -0.0669, -0.1417,\n",
-       "         -0.0960, -0.0696, -0.1411, -0.1129, -0.1242, -0.0905, -0.0864, -0.0537,\n",
-       "         -0.0715, -0.1260, -0.0893, -0.0724, -0.0545, -0.1258, -0.0956, -0.1273,\n",
-       "         -0.1076, -0.0027, -0.0948, -0.1091, -0.1213, -0.0926, -0.0830, -0.1056,\n",
-       "         -0.0157, -0.1370, -0.1040, -0.1043, -0.0478, -0.1154, -0.1032, -0.1521,\n",
-       "         -0.1198, -0.0703, -0.1244,  0.0165, -0.1011, -0.0815, -0.0712, -0.0906,\n",
-       "          0.0245, -0.0963, -0.0614, -0.0403, -0.0346, -0.1805, -0.1287, -0.1354,\n",
-       "         -0.0910, -0.1591, -0.1110, -0.1030, -0.2164, -0.1217,  0.0315, -0.1326,\n",
-       "         -0.0492, -0.0936, -0.1105, -0.1387, -0.0514, -0.1074, -0.1659, -0.0572,\n",
-       "         -0.1312, -0.0424, -0.1266, -0.0641, -0.1203, -0.0171,  0.0066, -0.1294,\n",
-       "          0.0158, -0.1282, -0.1711, -0.0258, -0.1567, -0.0794, -0.0934, -0.2338,\n",
-       "         -0.1127, -0.1702, -0.0654, -0.1358, -0.1117, -0.0531, -0.0871, -0.0803,\n",
-       "         -0.0796, -0.1043, -0.2110, -0.1153, -0.0302, -0.1105, -0.1396, -0.0989,\n",
-       "         -0.0764, -0.1203, -0.0862, -0.1803, -0.0762, -0.2285, -0.0974, -0.0589,\n",
-       "         -0.0619, -0.0592, -0.1120, -0.1702, -0.0994, -0.1272, -0.0899, -0.1679,\n",
-       "         -0.0787, -0.0908, -0.1409, -0.0739, -0.0683, -0.1159, -0.0963,  0.0453,\n",
-       "         -0.0288, -0.0702,  0.0430, -0.1654, -0.1037, -0.0073, -0.1259, -0.0697,\n",
-       "         -0.1191, -0.0978, -0.0498, -0.1115, -0.1244, -0.0291, -0.1179, -0.1194,\n",
-       "         -0.0681, -0.0270, -0.0875,  0.0183, -0.1697, -0.0688, -0.0978, -0.1144,\n",
-       "         -0.0920, -0.0483, -0.0356, -0.0141, -0.0773, -0.0777, -0.0749, -0.0268,\n",
-       "         -0.1100, -0.1829, -0.0981, -0.0480, -0.0778, -0.0762, -0.0397, -0.0575,\n",
-       "         -0.0912, -0.0268, -0.1617, -0.1517, -0.0550, -0.0881, -0.1129, -0.0509],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-0.0051]],\n",
-       " \n",
-       "          [[-0.0564]],\n",
-       " \n",
-       "          [[-0.0046]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0338]],\n",
-       " \n",
-       "          [[-0.0114]],\n",
-       " \n",
-       "          [[ 0.0060]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0348]],\n",
-       " \n",
-       "          [[-0.0265]],\n",
-       " \n",
-       "          [[ 0.0084]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0002]],\n",
-       " \n",
-       "          [[ 0.0397]],\n",
-       " \n",
-       "          [[-0.0011]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0578]],\n",
-       " \n",
-       "          [[ 0.0182]],\n",
-       " \n",
-       "          [[ 0.0406]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0673]],\n",
-       " \n",
-       "          [[ 0.0100]],\n",
-       " \n",
-       "          [[-0.0076]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0236]],\n",
-       " \n",
-       "          [[-0.0177]],\n",
-       " \n",
-       "          [[-0.0040]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0024]],\n",
-       " \n",
-       "          [[-0.0156]],\n",
-       " \n",
-       "          [[ 0.0333]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0119]],\n",
-       " \n",
-       "          [[-0.0033]],\n",
-       " \n",
-       "          [[ 0.0326]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0263]],\n",
-       " \n",
-       "          [[-0.0170]],\n",
-       " \n",
-       "          [[-0.0092]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0293]],\n",
-       " \n",
-       "          [[-0.0365]],\n",
-       " \n",
-       "          [[-0.0063]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0028]],\n",
-       " \n",
-       "          [[ 0.0239]],\n",
-       " \n",
-       "          [[ 0.0595]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.3193, 0.3699, 0.4947, 0.4148, 0.4479, 0.5682, 0.4690, 0.4059, 0.6563,\n",
-       "         0.5391, 0.4417, 0.4691, 0.3225, 0.4330, 0.3614, 0.7749, 0.4872, 0.4431,\n",
-       "         0.4017, 0.4328, 0.6053, 0.3767, 0.3971, 0.4646, 0.3329, 0.4837, 0.4767,\n",
-       "         0.4771, 0.3997, 0.6642, 0.3435, 0.4653, 0.4839, 0.3330, 0.3402, 0.4419,\n",
-       "         0.3403, 0.4499, 0.3581, 0.4142, 0.4437, 0.3064, 0.4156, 0.3977, 0.3871,\n",
-       "         0.4615, 0.5454, 0.6308, 0.4538, 0.4121, 0.4019, 0.5497, 0.4567, 0.4695,\n",
-       "         0.4778, 0.4255, 0.4741, 0.6359, 0.4833, 0.4474, 0.4476, 0.4642, 0.4611,\n",
-       "         0.4082, 0.3732, 0.4073, 0.3373, 0.5040, 0.4668, 0.4399, 0.4204, 0.4811,\n",
-       "         0.4426, 0.4133, 0.3749, 0.6139, 0.4280, 0.3091, 1.0831, 0.3068, 0.3743,\n",
-       "         0.5254, 0.3712, 0.4607, 0.4398, 0.5654, 0.4891, 0.4781, 0.4401, 0.5874,\n",
-       "         0.7453, 0.4619, 0.5490, 0.3756, 0.4228, 0.5612, 0.3954, 0.4391, 0.4350,\n",
-       "         0.4713, 0.3699, 0.5144, 0.4638, 0.3622, 0.3845, 0.4083, 0.3658, 0.3516,\n",
-       "         0.4049, 0.4096, 0.3324, 0.3740, 0.3738, 0.3332, 0.4521, 0.4183, 0.3671,\n",
-       "         0.2650, 0.5264, 0.3532, 0.3864, 0.5048, 0.4480, 0.3503, 0.4217, 0.3688,\n",
-       "         0.3744, 0.5150, 0.5386, 0.5516, 0.3805, 0.3191, 0.3341, 0.3405, 0.4049,\n",
-       "         0.5137, 0.3256, 0.5266, 0.3237, 0.3721, 0.4611, 0.3614, 0.4797, 0.4317,\n",
-       "         0.4014, 0.4822, 0.5661, 0.3779, 0.4510, 0.4167, 0.4010, 0.4948, 0.4310,\n",
-       "         0.4802, 0.3543, 0.3881, 0.4058, 0.4656, 0.4594, 0.4004, 0.5036, 0.4616,\n",
-       "         0.4185, 0.4727, 0.6697, 0.3430, 0.4320, 0.3578, 0.3269, 0.4253, 0.3212,\n",
-       "         0.4097, 0.4000, 0.4430, 0.4012, 0.3406, 0.6143, 0.4719, 0.4115, 0.3999,\n",
-       "         0.3688, 0.3775, 0.4181, 0.5439, 0.4703, 0.4549, 0.2905, 0.3966, 0.5014,\n",
-       "         0.5509, 0.4574, 0.5641, 0.3933, 0.4121, 0.5285, 0.5188, 0.3926, 0.3875,\n",
-       "         0.5861, 0.4348, 0.4483, 0.4165, 0.4370, 0.4284, 0.4111, 0.3580, 0.3650,\n",
-       "         0.3406, 0.5388, 0.3079, 0.4362, 0.3430, 0.5108, 0.4587, 0.4708, 0.4420,\n",
-       "         0.3876, 0.3186, 0.4856, 0.4796, 0.3340, 0.4327, 0.4745, 0.4257, 0.4843,\n",
-       "         0.4260, 0.3735, 0.3586, 0.3993, 0.4374, 0.5426, 0.4690, 0.5067, 0.3861,\n",
-       "         0.4784, 0.3938, 0.4554, 0.4756, 0.3839, 0.4036, 0.4134, 0.3833, 0.6873,\n",
-       "         0.3855, 0.5320, 0.3996, 0.4861, 0.4203, 0.3831, 0.3869, 0.4889, 0.4158,\n",
-       "         0.3239, 0.4658, 0.4903, 0.5728, 0.4976, 0.5894, 0.5113, 0.4462, 0.4155,\n",
-       "         0.4790, 0.4018, 0.4265, 0.4467, 0.3371, 0.4112, 0.3658, 0.3968, 0.4682,\n",
-       "         0.4262, 0.5130, 0.5301, 0.5273, 0.4356, 0.3839, 0.3936, 0.3545, 0.3893,\n",
-       "         0.4717, 0.3808, 0.5505, 0.4154, 0.3720, 0.3945, 0.4854, 0.1896, 0.4250,\n",
-       "         0.3550, 0.4554, 0.4027, 0.3518, 0.6171, 0.4207, 0.5002, 0.5947, 0.4739,\n",
-       "         0.3539, 0.1902, 0.3581, 0.3363, 0.4986, 0.4077, 0.4925, 0.3753, 0.4823,\n",
-       "         0.4115, 0.4449, 0.4733, 0.5363, 0.4349, 0.4261, 0.5428, 0.4357, 0.5223,\n",
-       "         0.5833, 0.5523, 0.3551, 0.4319, 0.4043, 0.5289, 0.4308, 0.3915, 0.2288,\n",
-       "         0.3759, 0.3725, 0.4608, 0.3945, 0.3181, 0.4846, 0.5233, 0.3806, 0.2042,\n",
-       "         0.3897, 0.3727, 0.3691, 0.5696, 0.5029, 0.3924, 0.4709, 0.5320, 0.5004,\n",
-       "         0.3477, 0.3993, 0.5092, 0.4060, 0.3752, 0.5474, 0.6146, 0.3866, 0.3777,\n",
-       "         0.3397, 0.6659, 0.4376, 0.3986, 0.4696, 0.3434, 0.5644, 0.5012, 0.5270,\n",
-       "         0.4104, 0.4929, 0.4263, 0.4955, 0.4812, 0.6116, 0.4358, 0.3580, 0.3682,\n",
-       "         0.4563, 0.4075, 0.5520, 0.3647, 0.3764, 0.5084, 0.3763, 0.3754, 0.3331,\n",
-       "         0.3631, 0.3153, 0.3917, 0.4095, 0.4067, 0.3277, 0.4872, 0.3774, 0.4172,\n",
-       "         0.3835, 0.4028, 0.3758, 0.3913, 0.5043, 0.6471, 0.4081, 0.4398, 0.4753,\n",
-       "         0.4132, 0.5298, 0.6552, 0.3859, 0.3113, 0.4360, 0.3461, 0.4259, 0.3861,\n",
-       "         0.4358, 0.4382, 0.4604, 0.3912, 0.3565, 0.4568, 0.5567, 0.4106, 0.2862,\n",
-       "         0.3890, 0.4693, 0.3783, 0.4323, 0.4754, 0.4285, 0.7533, 0.4796, 0.3249,\n",
-       "         0.5364, 0.4711, 0.4863, 0.4318, 0.3686, 0.3715, 0.4035, 0.3413, 0.3896,\n",
-       "         0.4226, 0.4424, 0.4028, 0.3804, 0.3391, 0.3605, 0.4314, 0.3949, 0.4044,\n",
-       "         0.3855, 0.3392, 0.6374, 0.3570, 1.0368, 0.5011, 0.4519, 0.5445, 0.4584,\n",
-       "         0.3766, 0.5019, 0.3892, 0.5165, 0.4819, 0.4141, 0.5431, 0.3636, 0.5195,\n",
-       "         0.3647, 0.5448, 0.4960, 0.4023, 0.2796, 0.4103, 0.3720, 0.3673, 0.5473,\n",
-       "         0.4929, 0.5189, 0.3636, 0.2956, 0.4198, 0.4069, 0.3284, 0.4414, 0.3588,\n",
-       "         0.4806, 0.4784, 0.4801, 0.3570, 0.6070, 0.3820, 0.3708, 0.3884, 0.3462,\n",
-       "         0.5242, 0.3491, 0.4878, 0.4112, 0.3616, 0.4345, 0.3836, 0.4424, 0.5946,\n",
-       "         0.3662, 0.4187, 0.4452, 0.5392, 0.4789, 0.4250, 0.3368, 0.3741, 0.3898,\n",
-       "         0.5580, 0.4831, 0.3695, 0.3816, 0.4102, 0.3537, 0.3953, 0.4485],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.0746, -0.1249, -0.1581, -0.0763, -0.0770, -0.0951, -0.1406, -0.0324,\n",
-       "         -0.1334, -0.0702, -0.0784, -0.1666, -0.0131, -0.1315, -0.1021, -0.1130,\n",
-       "          0.0048, -0.1203, -0.0950, -0.1735,  0.0663, -0.0821, -0.0353, -0.0819,\n",
-       "         -0.1141, -0.1661, -0.0794, -0.1360, -0.1013, -0.1492,  0.0341, -0.0986,\n",
-       "         -0.1157, -0.0875, -0.1145, -0.0401, -0.0099, -0.1313, -0.1183, -0.1569,\n",
-       "         -0.1525, -0.1086, -0.1493, -0.0954, -0.1428, -0.0994, -0.1944, -0.0182,\n",
-       "         -0.0763, -0.0941, -0.0968, -0.0352, -0.1753, -0.0701, -0.1315, -0.1441,\n",
-       "         -0.1017, -0.1790, -0.1882, -0.1211, -0.0910, -0.0641, -0.0600, -0.0956,\n",
-       "         -0.0697, -0.1949, -0.0652, -0.0779, -0.0915, -0.0924, -0.0959, -0.0679,\n",
-       "         -0.0985, -0.1308, -0.0841, -0.0260, -0.1210,  0.0138,  0.2261, -0.0901,\n",
-       "         -0.0638, -0.0957, -0.0456, -0.0872, -0.1177, -0.1745, -0.0431, -0.1083,\n",
-       "         -0.0142, -0.1748, -0.0327, -0.1127,  0.0066, -0.0477, -0.0981, -0.1665,\n",
-       "         -0.0711, -0.0584, -0.0459, -0.1330, -0.0952, -0.1632, -0.1898, -0.1315,\n",
-       "         -0.1107, -0.1625, -0.1117, -0.0894, -0.1421, -0.1346, -0.0743, -0.1761,\n",
-       "         -0.0205, -0.0312, -0.1321, -0.1059, -0.1236,  0.0791, -0.0579, -0.0283,\n",
-       "         -0.1148, -0.0618, -0.1328, -0.0641, -0.0984, -0.0574, -0.0889, -0.0061,\n",
-       "         -0.1529, -0.2062, -0.0970, -0.0640, -0.1053, -0.1218, -0.0922, -0.1553,\n",
-       "          0.0243, -0.0662, -0.0492, -0.1848, -0.0892, -0.0131, -0.1471, -0.1279,\n",
-       "         -0.1220, -0.1026, -0.0381, -0.1451, -0.0368, -0.1041, -0.0771, -0.1270,\n",
-       "         -0.1105,  0.0447, -0.1324, -0.1146, -0.0532, -0.0785, -0.0301, -0.0254,\n",
-       "         -0.0057, -0.0785, -0.1129, -0.1357, -0.1558, -0.1138, -0.0803, -0.0783,\n",
-       "         -0.0669, -0.1002, -0.1857, -0.1158, -0.0982, -0.1064, -0.0636, -0.0236,\n",
-       "         -0.0906, -0.1044, -0.1171, -0.1515, -0.1118, -0.1432, -0.1280, -0.1417,\n",
-       "         -0.1362, -0.0757, -0.1253, -0.0938, -0.1364, -0.1078, -0.0401, -0.1653,\n",
-       "         -0.1358, -0.1316, -0.1320, -0.0482, -0.0706, -0.0890, -0.1296, -0.0873,\n",
-       "         -0.0890, -0.1501, -0.1370, -0.0898, -0.1072, -0.0275, -0.0044, -0.0426,\n",
-       "         -0.1236, -0.0466, -0.1470, -0.1117, -0.1572, -0.0684, -0.0878, -0.1064,\n",
-       "         -0.1842, -0.1188, -0.0772, -0.1162, -0.1455,  0.1304, -0.1273, -0.1089,\n",
-       "         -0.1299, -0.1398, -0.0736, -0.1178, -0.0381, -0.1164, -0.1241, -0.0711,\n",
-       "         -0.1103, -0.0545, -0.0302, -0.0814, -0.0854, -0.1701, -0.0676, -0.0432,\n",
-       "         -0.0732, -0.0054, -0.1058, -0.0837, -0.1216, -0.1122, -0.1916, -0.0690,\n",
-       "         -0.0815, -0.0895, -0.0874, -0.1336, -0.0698, -0.0946, -0.0862, -0.0641,\n",
-       "         -0.1471, -0.1252, -0.1359, -0.0896, -0.0960, -0.1739, -0.1178, -0.0499,\n",
-       "          0.0058,  0.1777, -0.0859, -0.0604, -0.0778, -0.1612, -0.0578,  0.0105,\n",
-       "         -0.0809, -0.0510, -0.1499, -0.1185, -0.0947, -0.0797, -0.1398, -0.0963,\n",
-       "         -0.0461, -0.1857, -0.0876, -0.0642,  0.2263, -0.1416,  0.1731, -0.0672,\n",
-       "         -0.0961, -0.1842, -0.1553, -0.0556, -0.0590, -0.0616, -0.1188, -0.1709,\n",
-       "         -0.1434, -0.0966,  0.2476, -0.1056, -0.1430, -0.1294, -0.0701, -0.1234,\n",
-       "         -0.0666, -0.0961, -0.1301, -0.1044, -0.1442, -0.0197, -0.0737, -0.1153,\n",
-       "         -0.1270, -0.1448, -0.1786, -0.1814, -0.0741, -0.1119, -0.0764, -0.0605,\n",
-       "         -0.0839, -0.1996, -0.0933,  0.1616, -0.0515, -0.1245, -0.0286, -0.0910,\n",
-       "         -0.0901, -0.0276, -0.0470, -0.0769,  0.0207, -0.1004, -0.0904, -0.1074,\n",
-       "         -0.1872, -0.1303, -0.1112, -0.1033, -0.1274, -0.0733, -0.0669, -0.1417,\n",
-       "         -0.0960, -0.0696, -0.1411, -0.1129, -0.1242, -0.0905, -0.0864, -0.0537,\n",
-       "         -0.0715, -0.1260, -0.0893, -0.0724, -0.0545, -0.1258, -0.0956, -0.1273,\n",
-       "         -0.1076, -0.0027, -0.0948, -0.1091, -0.1213, -0.0926, -0.0830, -0.1056,\n",
-       "         -0.0157, -0.1370, -0.1040, -0.1043, -0.0478, -0.1154, -0.1032, -0.1521,\n",
-       "         -0.1198, -0.0703, -0.1244,  0.0165, -0.1011, -0.0815, -0.0712, -0.0906,\n",
-       "          0.0245, -0.0963, -0.0614, -0.0403, -0.0346, -0.1805, -0.1287, -0.1354,\n",
-       "         -0.0910, -0.1591, -0.1110, -0.1030, -0.2164, -0.1217,  0.0315, -0.1326,\n",
-       "         -0.0492, -0.0936, -0.1105, -0.1387, -0.0514, -0.1074, -0.1659, -0.0572,\n",
-       "         -0.1312, -0.0424, -0.1266, -0.0641, -0.1203, -0.0171,  0.0066, -0.1294,\n",
-       "          0.0158, -0.1282, -0.1711, -0.0258, -0.1567, -0.0794, -0.0934, -0.2338,\n",
-       "         -0.1127, -0.1702, -0.0654, -0.1358, -0.1117, -0.0531, -0.0871, -0.0803,\n",
-       "         -0.0796, -0.1043, -0.2110, -0.1153, -0.0302, -0.1105, -0.1396, -0.0989,\n",
-       "         -0.0764, -0.1203, -0.0862, -0.1803, -0.0762, -0.2285, -0.0974, -0.0589,\n",
-       "         -0.0619, -0.0592, -0.1120, -0.1702, -0.0994, -0.1272, -0.0899, -0.1679,\n",
-       "         -0.0787, -0.0908, -0.1409, -0.0739, -0.0683, -0.1159, -0.0963,  0.0453,\n",
-       "         -0.0288, -0.0702,  0.0430, -0.1654, -0.1037, -0.0073, -0.1259, -0.0697,\n",
-       "         -0.1191, -0.0978, -0.0498, -0.1115, -0.1244, -0.0291, -0.1179, -0.1194,\n",
-       "         -0.0681, -0.0270, -0.0875,  0.0183, -0.1697, -0.0688, -0.0978, -0.1144,\n",
-       "         -0.0920, -0.0483, -0.0356, -0.0141, -0.0773, -0.0777, -0.0749, -0.0268,\n",
-       "         -0.1100, -0.1829, -0.0981, -0.0480, -0.0778, -0.0762, -0.0397, -0.0575,\n",
-       "         -0.0912, -0.0268, -0.1617, -0.1517, -0.0550, -0.0881, -0.1129, -0.0509],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 1.2985e-02,  5.8775e-03,  1.6480e-02],\n",
-       "           [ 9.8274e-03,  8.5853e-03,  2.5142e-02],\n",
-       "           [-1.0668e-03,  5.6142e-03,  1.9953e-02]],\n",
-       " \n",
-       "          [[-4.8181e-03, -5.3413e-03,  6.3347e-04],\n",
-       "           [-1.3625e-02, -1.6496e-02, -2.1865e-03],\n",
-       "           [ 1.5723e-03, -1.7390e-02,  7.4517e-03]],\n",
-       " \n",
-       "          [[ 2.0447e-02,  1.9393e-02,  9.7699e-03],\n",
-       "           [ 2.8801e-03,  7.6073e-04,  1.6004e-03],\n",
-       "           [ 7.1150e-03,  4.3094e-03,  7.8511e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.8874e-02,  2.6493e-02,  1.8950e-02],\n",
-       "           [ 9.7234e-03,  2.5863e-02,  1.5245e-02],\n",
-       "           [ 1.7844e-02,  2.8209e-02,  2.1543e-02]],\n",
-       " \n",
-       "          [[-2.2758e-03, -3.1559e-04,  1.0536e-02],\n",
-       "           [-3.5612e-02, -4.0082e-02, -2.9468e-02],\n",
-       "           [-2.1786e-02, -3.3162e-02, -2.7458e-02]],\n",
-       " \n",
-       "          [[-2.3736e-02, -1.8665e-02, -6.3038e-03],\n",
-       "           [-4.3088e-03,  1.2654e-02,  1.3828e-02],\n",
-       "           [ 6.4535e-03,  1.2097e-02,  1.9104e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.0882e-02,  2.0081e-02,  1.3910e-02],\n",
-       "           [ 5.0733e-03,  1.9578e-02,  1.2557e-02],\n",
-       "           [-1.0346e-02, -3.3292e-03, -1.6335e-02]],\n",
-       " \n",
-       "          [[-7.9042e-03, -8.4392e-03, -7.6487e-03],\n",
-       "           [-7.9495e-03, -5.5225e-03, -2.0337e-02],\n",
-       "           [-9.5581e-03, -3.0733e-03, -2.7514e-03]],\n",
-       " \n",
-       "          [[-3.4704e-02, -4.5196e-02, -2.0394e-02],\n",
-       "           [-2.7918e-02, -5.1510e-02, -2.9681e-02],\n",
-       "           [ 8.1949e-04, -1.5416e-02, -6.7506e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 8.6308e-03,  8.3677e-03,  3.9789e-03],\n",
-       "           [-1.4876e-02, -2.0422e-02, -1.2444e-02],\n",
-       "           [-5.6083e-03, -2.5871e-02, -9.7503e-03]],\n",
-       " \n",
-       "          [[-9.3156e-03, -1.5460e-02, -1.6127e-02],\n",
-       "           [ 4.6098e-03,  6.3302e-03,  1.2090e-02],\n",
-       "           [ 1.8281e-02,  1.6466e-02,  1.6838e-02]],\n",
-       " \n",
-       "          [[ 1.3668e-02,  1.8402e-02,  1.6503e-02],\n",
-       "           [ 1.2338e-02,  1.2511e-02,  1.9672e-02],\n",
-       "           [ 4.7979e-03, -2.3567e-04,  6.6718e-04]]],\n",
-       " \n",
-       " \n",
-       "         [[[-3.7336e-03, -1.0346e-02, -1.9740e-02],\n",
-       "           [ 9.9717e-03,  1.5948e-02,  4.2643e-03],\n",
-       "           [ 7.6492e-03,  7.1111e-04, -2.6102e-03]],\n",
-       " \n",
-       "          [[-8.2924e-03, -1.5823e-02, -2.7724e-02],\n",
-       "           [-2.6238e-02, -3.1989e-02, -3.6157e-02],\n",
-       "           [-2.3211e-02, -2.4658e-02, -2.2340e-02]],\n",
-       " \n",
-       "          [[-3.9138e-02, -4.0273e-02, -3.1117e-02],\n",
-       "           [-1.6803e-02, -1.7937e-02, -2.2626e-02],\n",
-       "           [-1.3934e-02, -2.1421e-02, -1.6982e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.1366e-02, -3.1674e-02, -2.7122e-02],\n",
-       "           [-1.3105e-02, -1.9811e-02, -1.9967e-02],\n",
-       "           [-3.2530e-04, -7.9953e-03, -2.1114e-02]],\n",
-       " \n",
-       "          [[-3.4095e-03, -1.4877e-02, -1.6509e-03],\n",
-       "           [ 6.7661e-03, -5.8129e-03, -2.5779e-03],\n",
-       "           [ 9.8352e-03, -3.5613e-04, -4.7214e-03]],\n",
-       " \n",
-       "          [[-1.8239e-05, -1.6624e-02, -7.5130e-03],\n",
-       "           [ 9.0699e-03,  1.0587e-02, -5.6982e-04],\n",
-       "           [ 2.2772e-02,  2.8874e-02,  1.3017e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-9.7669e-03, -8.8238e-04, -5.7930e-03],\n",
-       "           [-2.4941e-02, -8.6098e-03, -1.1588e-02],\n",
-       "           [-2.5119e-02, -3.0357e-02, -2.9953e-02]],\n",
-       " \n",
-       "          [[-1.5327e-02, -1.8215e-02, -1.2769e-02],\n",
-       "           [-2.5723e-02, -3.4062e-02, -1.7149e-02],\n",
-       "           [-1.8415e-02, -3.0589e-02, -1.9719e-02]],\n",
-       " \n",
-       "          [[ 3.5881e-03,  5.1544e-03,  1.8429e-03],\n",
-       "           [ 1.1056e-03, -3.2708e-03, -3.6205e-03],\n",
-       "           [-3.6405e-03, -1.6056e-02, -4.9827e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.5401e-02, -1.8940e-02, -1.1045e-02],\n",
-       "           [-1.8884e-02, -1.7063e-02, -3.6007e-03],\n",
-       "           [-5.4442e-03, -1.6594e-02, -1.4981e-02]],\n",
-       " \n",
-       "          [[-1.4496e-02, -1.8457e-02, -9.5360e-04],\n",
-       "           [-2.7743e-03, -3.9637e-03,  1.2801e-02],\n",
-       "           [ 4.0058e-04,  4.0636e-04,  1.2980e-02]],\n",
-       " \n",
-       "          [[ 6.7257e-03,  2.5009e-02,  1.1982e-02],\n",
-       "           [ 1.3225e-02,  2.4900e-02,  2.0167e-03],\n",
-       "           [ 8.9244e-03,  2.1907e-02, -5.1172e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-9.4444e-03, -7.7491e-03, -1.2832e-02],\n",
-       "           [ 6.8632e-05, -2.8702e-03, -8.0025e-03],\n",
-       "           [ 1.8781e-02,  1.7544e-02,  8.1965e-03]],\n",
-       " \n",
-       "          [[-1.8037e-02, -2.5060e-03,  1.0862e-02],\n",
-       "           [-5.5585e-03,  4.9919e-03,  1.0392e-02],\n",
-       "           [ 6.4507e-03,  7.2493e-04, -2.0803e-02]],\n",
-       " \n",
-       "          [[-7.7808e-03,  6.4229e-03,  2.6337e-02],\n",
-       "           [-2.0836e-02, -1.7037e-02,  1.5743e-02],\n",
-       "           [-2.3798e-02, -1.1823e-02,  2.4214e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.5576e-02,  3.8835e-03,  1.7293e-02],\n",
-       "           [-2.8924e-03, -9.1514e-04,  1.1430e-02],\n",
-       "           [-7.8650e-03, -1.7250e-02,  7.0872e-03]],\n",
-       " \n",
-       "          [[ 4.6681e-04, -8.2603e-03, -4.0696e-03],\n",
-       "           [ 1.1329e-02, -8.6356e-03, -3.3255e-03],\n",
-       "           [ 6.9890e-03, -1.8588e-03, -2.4809e-03]],\n",
-       " \n",
-       "          [[-1.5393e-02, -2.9431e-03,  1.4021e-03],\n",
-       "           [-4.4720e-03, -5.4175e-03, -7.5443e-03],\n",
-       "           [-1.1494e-02, -2.7088e-03,  1.1926e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 4.0966e-03,  1.0570e-02,  2.1981e-02],\n",
-       "           [ 2.5834e-02,  2.1339e-02,  1.8383e-02],\n",
-       "           [ 3.0461e-02,  3.3067e-02,  1.7232e-02]],\n",
-       " \n",
-       "          [[-5.6481e-03, -1.3997e-02, -1.0151e-02],\n",
-       "           [ 8.0715e-04, -6.8213e-03, -1.4015e-02],\n",
-       "           [ 7.7966e-03, -3.4988e-03, -3.8892e-03]],\n",
-       " \n",
-       "          [[ 1.8418e-02,  1.6268e-02, -1.9658e-03],\n",
-       "           [ 4.5004e-03,  1.8793e-02,  1.8512e-02],\n",
-       "           [ 1.0995e-03,  1.4834e-02,  2.9219e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 3.7154e-02,  3.7846e-02,  3.6304e-02],\n",
-       "           [ 2.6090e-02,  2.6580e-02,  3.7068e-02],\n",
-       "           [-1.2169e-02, -1.4939e-02,  1.2062e-02]],\n",
-       " \n",
-       "          [[ 1.2454e-02, -1.9622e-03,  2.9848e-03],\n",
-       "           [-1.5304e-02, -2.8096e-02, -6.9638e-03],\n",
-       "           [-4.3663e-03, -1.1101e-02, -8.0610e-03]],\n",
-       " \n",
-       "          [[ 3.8935e-03,  6.5232e-03,  7.1197e-03],\n",
-       "           [-1.2163e-02, -7.5848e-03, -1.1456e-02],\n",
-       "           [-7.1280e-03, -4.8672e-03, -5.1746e-03]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2508, 0.2219, 0.2197, 0.2654, 0.2848, 0.2232, 0.3639, 0.2556, 0.1851,\n",
-       "         0.2583, 0.2076, 0.2817, 0.2639, 0.1712, 0.2418, 0.1593, 0.2374, 0.2501,\n",
-       "         0.2158, 0.2359, 0.2363, 0.1930, 0.2072, 0.2214, 0.3103, 0.2346, 0.2793,\n",
-       "         0.2299, 0.2273, 0.2276, 0.2443, 0.2531, 0.2267, 0.2053, 0.2232, 0.2617,\n",
-       "         0.2638, 0.2643, 0.2598, 0.3110, 0.2888, 0.2105, 0.1918, 0.2349, 0.2249,\n",
-       "         0.2466, 0.2732, 0.2319, 0.2232, 0.2346, 0.1832, 0.2649, 0.2060, 0.2460,\n",
-       "         0.2358, 0.2198, 0.2237, 0.2392, 0.2535, 0.2272, 0.2238, 0.2194, 0.3159,\n",
-       "         0.2378, 0.2138, 0.1714, 0.2133, 0.2600, 0.2607, 0.2129, 0.2238, 0.2000,\n",
-       "         0.2211, 0.2675, 0.2303, 0.2305, 0.2723, 0.2304, 0.2223, 0.2656, 0.2636,\n",
-       "         0.1956, 0.2297, 0.2150, 0.2501, 0.2262, 0.1962, 0.2347, 0.1258, 0.1791,\n",
-       "         0.2361, 0.2093, 0.2190, 0.2773, 0.2805, 0.2708, 0.2378, 0.2097, 0.2312,\n",
-       "         0.2369, 0.2406, 0.2099, 0.2053, 0.2315, 0.2332, 0.2172, 0.2249, 0.2312,\n",
-       "         0.2005, 0.2231, 0.2082, 0.2665, 0.2244, 0.2375, 0.2064, 0.2166, 0.3192,\n",
-       "         0.2521, 0.2521, 0.2587, 0.1963, 0.2264, 0.2318, 0.1915, 0.2253, 0.2367,\n",
-       "         0.2041, 0.2178, 0.2425, 0.2421, 0.2383, 0.2273, 0.2480, 0.2425, 0.2256,\n",
-       "         0.2124, 0.2082, 0.2410, 0.2412, 0.2954, 0.2312, 0.2546, 0.2336, 0.2333,\n",
-       "         0.2791, 0.2147, 0.2536, 0.2282, 0.2554, 0.2286, 0.2316, 0.2387, 0.2445,\n",
-       "         0.2439, 0.2597, 0.2038, 0.2806, 0.2076, 0.2550, 0.2122, 0.1971, 0.2032,\n",
-       "         0.2340, 0.1962, 0.1984, 0.2314, 0.2234, 0.2448, 0.2237, 0.2297, 0.3105,\n",
-       "         0.2732, 0.2641, 0.2469, 0.2513, 0.2664, 0.2351, 0.2015, 0.2515, 0.2723,\n",
-       "         0.2604, 0.2554, 0.2182, 0.2197, 0.2125, 0.2686, 0.2549, 0.2038, 0.2116,\n",
-       "         0.2262, 0.2402, 0.2182, 0.2182, 0.2248, 0.1774, 0.2762, 0.2257, 0.2417,\n",
-       "         0.2417, 0.1909, 0.2039, 0.2317, 0.2312, 0.2261, 0.2362, 0.2394, 0.2788,\n",
-       "         0.2193, 0.2633, 0.1303, 0.2367, 0.2270, 0.2794, 0.2799, 0.1860, 0.2034,\n",
-       "         0.2348, 0.2639, 0.2183, 0.3326, 0.2913, 0.2295, 0.2176, 0.2509, 0.2099,\n",
-       "         0.2068, 0.2526, 0.2500, 0.2231, 0.2252, 0.2079, 0.2081, 0.2013, 0.2282,\n",
-       "         0.2480, 0.2561, 0.2435, 0.2328, 0.2303, 0.2296, 0.2713, 0.2329, 0.2362,\n",
-       "         0.2589, 0.1890, 0.2166, 0.2416, 0.2114, 0.2574, 0.2246, 0.2412, 0.2267,\n",
-       "         0.2349, 0.2100, 0.1896, 0.2376, 0.2318, 0.2851, 0.2105, 0.2387, 0.2284,\n",
-       "         0.2061, 0.1741, 0.2446, 0.2374, 0.2086, 0.2217, 0.2284, 0.2346, 0.2271,\n",
-       "         0.2366, 0.2056, 0.2282, 0.2331, 0.2915, 0.1509, 0.2154, 0.2541, 0.2252,\n",
-       "         0.2448, 0.2178, 0.2573, 0.1811, 0.2106, 0.2614, 0.2020, 0.2510, 0.2195,\n",
-       "         0.1964, 0.2206, 0.2272, 0.2420, 0.2415, 0.1962, 0.2776, 0.2390, 0.2659,\n",
-       "         0.2587, 0.2084, 0.2569, 0.2499, 0.2401, 0.1946, 0.2232, 0.2308, 0.2552,\n",
-       "         0.2209, 0.2067, 0.2327, 0.2285, 0.1873, 0.2544, 0.2203, 0.2681, 0.2040,\n",
-       "         0.2732, 0.2045, 0.2400, 0.2691, 0.2374, 0.2662, 0.1850, 0.1997, 0.2045,\n",
-       "         0.2373, 0.2332, 0.2414, 0.2186, 0.2345, 0.2154, 0.2998, 0.2415, 0.2513,\n",
-       "         0.2491, 0.2243, 0.2322, 0.2588, 0.2538, 0.2268, 0.2145, 0.2193, 0.2389,\n",
-       "         0.2268, 0.2371, 0.2720, 0.2664, 0.2394, 0.2485, 0.2255, 0.2157, 0.2856,\n",
-       "         0.2198, 0.2572, 0.2317, 0.1513, 0.2587, 0.2466, 0.3025, 0.2137, 0.2072,\n",
-       "         0.2486, 0.2054, 0.2633, 0.2875, 0.2926, 0.1949, 0.2580, 0.2377, 0.2037,\n",
-       "         0.2252, 0.2318, 0.1427, 0.2101, 0.2000, 0.1976, 0.2333, 0.2552, 0.2228,\n",
-       "         0.2484, 0.2607, 0.2770, 0.1829, 0.2098, 0.2006, 0.2322, 0.2518, 0.2680,\n",
-       "         0.2328, 0.2104, 0.1935, 0.2480, 0.2517, 0.2382, 0.2283, 0.2256, 0.2437,\n",
-       "         0.2657, 0.2297, 0.2082, 0.2398, 0.2685, 0.2294, 0.1731, 0.2130, 0.2048,\n",
-       "         0.2383, 0.2511, 0.2332, 0.2420, 0.2123, 0.2179, 0.2088, 0.2264, 0.2864,\n",
-       "         0.2332, 0.2363, 0.1422, 0.2293, 0.2357, 0.2316, 0.2508, 0.2726, 0.2088,\n",
-       "         0.2122, 0.2388, 0.2252, 0.2438, 0.2164, 0.2476, 0.2589, 0.2048, 0.2251,\n",
-       "         0.2126, 0.1937, 0.2384, 0.1916, 0.2327, 0.2261, 0.1992, 0.2346, 0.2146,\n",
-       "         0.2218, 0.2246, 0.2384, 0.2988, 0.2479, 0.2352, 0.2249, 0.2524, 0.2355,\n",
-       "         0.2307, 0.2883, 0.2280, 0.3037, 0.2703, 0.2252, 0.2144, 0.2334, 0.2352,\n",
-       "         0.2036, 0.2156, 0.2162, 0.2534, 0.1831, 0.2804, 0.2074, 0.2145, 0.2512,\n",
-       "         0.2518, 0.1931, 0.2643, 0.2882, 0.1841, 0.2622, 0.2042, 0.2441, 0.2190,\n",
-       "         0.2117, 0.2011, 0.2637, 0.2388, 0.2751, 0.2396, 0.2069, 0.2129, 0.2409,\n",
-       "         0.2313, 0.2364, 0.2160, 0.2589, 0.2255, 0.2669, 0.2023, 0.2739, 0.2628,\n",
-       "         0.2629, 0.2350, 0.2478, 0.2856, 0.2879, 0.2146, 0.1386, 0.2330, 0.2150,\n",
-       "         0.2231, 0.2338, 0.1927, 0.2003, 0.2258, 0.2535, 0.2176, 0.2297],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.2642, -0.2527, -0.1893, -0.2419, -0.3290, -0.2181, -0.1712, -0.2358,\n",
-       "         -0.1613, -0.2113, -0.1717, -0.2953, -0.2448, -0.1493, -0.2419, -0.1487,\n",
-       "         -0.2250, -0.2635, -0.2298, -0.2116, -0.2127, -0.1430, -0.1944, -0.2372,\n",
-       "         -0.3841, -0.1962, -0.3026, -0.2201, -0.2175, -0.1737, -0.2495, -0.2478,\n",
-       "         -0.1895, -0.2260, -0.1891, -0.2291, -0.2712, -0.2878, -0.2329, -0.3227,\n",
-       "         -0.2869, -0.1869, -0.1415, -0.1465, -0.2215, -0.2601, -0.2591, -0.2554,\n",
-       "         -0.1616, -0.1884, -0.1617, -0.2770, -0.2103, -0.2326, -0.2663, -0.2253,\n",
-       "         -0.1691, -0.2290, -0.1952, -0.1997, -0.2044, -0.1908, -0.3289, -0.1634,\n",
-       "         -0.1918, -0.1498, -0.1320, -0.2258, -0.2140, -0.1883, -0.1671, -0.1635,\n",
-       "         -0.2101, -0.2948, -0.2372, -0.2358, -0.2733, -0.2011, -0.1994, -0.2577,\n",
-       "         -0.3001, -0.1664, -0.2154, -0.1739, -0.2238, -0.2210, -0.2028, -0.2635,\n",
-       "          0.0424, -0.1290, -0.2377, -0.1980, -0.2281, -0.3019, -0.2907, -0.2393,\n",
-       "         -0.2098, -0.2044, -0.1945, -0.2132, -0.2214, -0.1184, -0.2101, -0.2151,\n",
-       "         -0.1309, -0.2024, -0.1622, -0.2164, -0.1914, -0.1935, -0.1513, -0.2919,\n",
-       "         -0.2358, -0.1787, -0.1504, -0.2128, -0.2973, -0.2652, -0.2819, -0.2250,\n",
-       "         -0.2108, -0.2058, -0.1789, -0.2132, -0.2196, -0.2354, -0.2160, -0.2032,\n",
-       "         -0.2328, -0.1791, -0.2591, -0.2119, -0.2534, -0.2412, -0.2399, -0.1671,\n",
-       "         -0.2534, -0.1876, -0.2052, -0.2842, -0.1861, -0.2700, -0.1834, -0.1790,\n",
-       "         -0.2043, -0.2294, -0.2924, -0.1894, -0.2618, -0.1867, -0.2177, -0.2283,\n",
-       "         -0.1959, -0.2075, -0.2705, -0.1711, -0.3199, -0.1389, -0.2947, -0.1007,\n",
-       "         -0.1618, -0.1963, -0.1315, -0.1541, -0.1687, -0.2003, -0.1655, -0.2637,\n",
-       "         -0.1649, -0.1506, -0.3189, -0.2556, -0.2595, -0.2397, -0.2529, -0.2619,\n",
-       "         -0.2132, -0.1645, -0.2435, -0.2275, -0.2027, -0.2452, -0.1668, -0.2208,\n",
-       "         -0.1667, -0.2866, -0.2471, -0.1851, -0.2250, -0.1952, -0.1937, -0.2048,\n",
-       "         -0.1271, -0.1905, -0.2267, -0.3031, -0.1649, -0.2091, -0.2611, -0.1740,\n",
-       "         -0.1731, -0.2341, -0.2184, -0.2151, -0.1726, -0.2258, -0.3636, -0.1991,\n",
-       "         -0.1991,  0.0321, -0.2124, -0.1771, -0.3231, -0.2806, -0.1842, -0.1508,\n",
-       "         -0.2681, -0.3103, -0.1813, -0.3850, -0.3016, -0.2180, -0.1782, -0.2616,\n",
-       "         -0.1916, -0.1248, -0.2586, -0.2025, -0.2251, -0.2301, -0.1968, -0.1476,\n",
-       "         -0.1654, -0.1908, -0.2085, -0.2473, -0.2567, -0.2444, -0.2748, -0.2375,\n",
-       "         -0.3244, -0.1584, -0.1855, -0.2336, -0.1956, -0.1538, -0.2145, -0.1423,\n",
-       "         -0.1895, -0.1666, -0.1841, -0.2300, -0.2189, -0.1339, -0.1370, -0.2215,\n",
-       "         -0.2106, -0.3307, -0.1702, -0.2305, -0.2649, -0.1639, -0.1599, -0.1993,\n",
-       "         -0.2351, -0.1454, -0.1876, -0.2112, -0.2334, -0.2129, -0.2566, -0.1590,\n",
-       "         -0.1841, -0.2310, -0.3050, -0.1083, -0.2167, -0.2396, -0.2562, -0.1862,\n",
-       "         -0.1588, -0.2464, -0.0957, -0.1762, -0.2666, -0.2150, -0.2497, -0.2214,\n",
-       "         -0.1581, -0.1909, -0.1520, -0.1859, -0.2028, -0.1746, -0.2784, -0.2447,\n",
-       "         -0.3235, -0.2106, -0.2028, -0.2292, -0.2557, -0.2728, -0.1934, -0.2204,\n",
-       "         -0.1648, -0.2352, -0.2067, -0.2216, -0.2174, -0.2260, -0.1763, -0.2607,\n",
-       "         -0.1793, -0.2857, -0.1195, -0.2755, -0.1421, -0.2307, -0.2690, -0.2265,\n",
-       "         -0.2339, -0.1669, -0.1676, -0.1983, -0.2476, -0.1643, -0.1754, -0.1409,\n",
-       "         -0.2250, -0.1616, -0.3808, -0.2011, -0.2716, -0.2373, -0.1867, -0.2292,\n",
-       "         -0.2468, -0.3056, -0.1959, -0.1689, -0.1812, -0.2078, -0.1987, -0.2124,\n",
-       "         -0.2680, -0.3007, -0.2063, -0.2463, -0.2377, -0.2058, -0.2839, -0.2105,\n",
-       "         -0.2381, -0.1881, -0.0486, -0.2375, -0.2057, -0.3259, -0.1952, -0.1850,\n",
-       "         -0.2531, -0.1927, -0.2695, -0.2596, -0.2798, -0.1535, -0.2369, -0.1918,\n",
-       "         -0.2141, -0.1724, -0.2102, -0.0114, -0.1611, -0.1270, -0.1965, -0.2115,\n",
-       "         -0.2090, -0.1902, -0.2637, -0.2648, -0.2042, -0.1769, -0.2044, -0.1757,\n",
-       "         -0.2109, -0.2072, -0.2763, -0.2187, -0.1723, -0.1538, -0.3108, -0.2214,\n",
-       "         -0.2091, -0.2362, -0.2377, -0.2577, -0.1999, -0.2202, -0.1586, -0.2214,\n",
-       "         -0.2893, -0.2537, -0.1394, -0.1940, -0.1540, -0.2383, -0.2384, -0.1676,\n",
-       "         -0.2245, -0.2531, -0.1570, -0.1384, -0.2034, -0.3143, -0.2063, -0.2070,\n",
-       "          0.0603, -0.2318, -0.2447, -0.2531, -0.2264, -0.2420, -0.1702, -0.2374,\n",
-       "         -0.2420, -0.2345, -0.2003, -0.2153, -0.2455, -0.1950, -0.1682, -0.2212,\n",
-       "         -0.1737, -0.1483, -0.1795, -0.1750, -0.1716, -0.1845, -0.1758, -0.1818,\n",
-       "         -0.1668, -0.2203, -0.1737, -0.2799, -0.3223, -0.2174, -0.2284, -0.2392,\n",
-       "         -0.1516, -0.2313, -0.2142, -0.2659, -0.2415, -0.3303, -0.2774, -0.2035,\n",
-       "         -0.2008, -0.2303, -0.1995, -0.1958, -0.1755, -0.2234, -0.2544, -0.1453,\n",
-       "         -0.2332, -0.1429, -0.1991, -0.2164, -0.2177, -0.1526, -0.2944, -0.2597,\n",
-       "         -0.2154, -0.2397, -0.1352, -0.2052, -0.1534, -0.1752, -0.2029, -0.3306,\n",
-       "         -0.2709, -0.2928, -0.1451, -0.1637, -0.1974, -0.2433, -0.2276, -0.1428,\n",
-       "         -0.1217, -0.2319, -0.2483, -0.2990, -0.1571, -0.2614, -0.2736, -0.2439,\n",
-       "         -0.1627, -0.2943, -0.2589, -0.2840, -0.1815, -0.0638, -0.2827, -0.2453,\n",
-       "         -0.1847, -0.2279, -0.1897, -0.1714, -0.2150, -0.2159, -0.1272, -0.1962],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 1.0158e-02,  1.5344e-02,  1.0404e-02],\n",
-       "           [ 6.3898e-03,  1.8127e-02,  2.1689e-02],\n",
-       "           [ 1.2039e-02,  9.1765e-03,  1.2555e-02]],\n",
-       " \n",
-       "          [[-6.6464e-03,  4.2054e-03,  7.1157e-03],\n",
-       "           [ 6.3941e-04,  8.7883e-03,  1.2527e-02],\n",
-       "           [-1.5434e-02, -7.4481e-03, -3.5093e-03]],\n",
-       " \n",
-       "          [[-3.9378e-03, -5.9819e-03, -1.3043e-02],\n",
-       "           [ 2.7636e-03,  2.8665e-03, -4.5871e-04],\n",
-       "           [-3.4836e-03, -4.1312e-03, -5.7626e-04]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-9.9531e-03, -7.6999e-03, -2.3074e-03],\n",
-       "           [-4.6409e-03, -1.3611e-02, -3.6575e-03],\n",
-       "           [-3.1854e-03, -1.0017e-02,  1.8804e-03]],\n",
-       " \n",
-       "          [[ 3.5398e-02,  3.9781e-02,  1.8116e-02],\n",
-       "           [ 3.2292e-02,  2.7703e-02,  1.0273e-02],\n",
-       "           [-5.9668e-05,  1.6928e-02,  1.1155e-02]],\n",
-       " \n",
-       "          [[ 2.0612e-02,  3.6208e-02,  3.3401e-02],\n",
-       "           [ 2.5563e-02,  4.3201e-02,  4.0211e-02],\n",
-       "           [-6.1129e-03,  1.7931e-04,  8.8326e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[-4.2663e-03, -6.6279e-03, -1.7188e-02],\n",
-       "           [-1.4775e-02, -2.3089e-02, -1.9743e-02],\n",
-       "           [-1.1323e-02, -1.6064e-02, -9.3290e-03]],\n",
-       " \n",
-       "          [[ 8.2665e-03, -2.9106e-04,  1.0408e-02],\n",
-       "           [ 9.8399e-04, -6.5159e-03, -8.9978e-03],\n",
-       "           [ 9.4474e-03,  5.0895e-03, -4.0562e-04]],\n",
-       " \n",
-       "          [[-4.3403e-03,  1.5065e-03,  8.5327e-03],\n",
-       "           [-9.9096e-03, -1.5336e-02, -1.0836e-02],\n",
-       "           [-6.1585e-03, -1.5817e-02, -9.6927e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 6.8123e-03,  1.1000e-02,  1.1369e-02],\n",
-       "           [-5.4612e-04,  2.1832e-03,  3.9948e-03],\n",
-       "           [ 1.4182e-02,  1.9440e-02,  1.4985e-02]],\n",
-       " \n",
-       "          [[ 4.9183e-03,  6.3152e-04,  9.6661e-04],\n",
-       "           [ 9.9695e-03,  1.3853e-02,  1.4104e-02],\n",
-       "           [ 2.1778e-02,  2.2043e-02,  4.2299e-04]],\n",
-       " \n",
-       "          [[-1.0525e-02, -1.8252e-02, -1.9766e-02],\n",
-       "           [-4.4405e-03, -1.3726e-02, -1.1965e-02],\n",
-       "           [ 1.8558e-04, -5.5566e-03,  5.2900e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 7.3021e-03, -4.2738e-05, -2.9690e-03],\n",
-       "           [-1.1109e-02, -1.3191e-02, -5.2006e-03],\n",
-       "           [-3.3410e-03, -4.7529e-03, -5.6999e-03]],\n",
-       " \n",
-       "          [[-2.3208e-03, -9.9885e-03, -6.6940e-03],\n",
-       "           [ 1.4011e-02,  5.8402e-03, -2.7200e-04],\n",
-       "           [ 1.7060e-02,  8.0047e-03,  3.5731e-03]],\n",
-       " \n",
-       "          [[-1.7324e-02, -1.0082e-02, -1.1286e-02],\n",
-       "           [-1.0802e-02, -7.8075e-03, -3.1911e-03],\n",
-       "           [-1.1623e-02, -7.1699e-03, -1.5279e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.5339e-02, -2.0109e-02, -1.2339e-02],\n",
-       "           [-1.3728e-02, -2.0620e-02, -1.8007e-02],\n",
-       "           [-1.3095e-02, -1.2404e-02, -6.5197e-03]],\n",
-       " \n",
-       "          [[ 4.9673e-03,  2.9987e-03,  2.0941e-03],\n",
-       "           [ 3.4844e-03, -2.0313e-03, -2.8811e-03],\n",
-       "           [ 1.1634e-02,  4.0999e-03, -2.8989e-03]],\n",
-       " \n",
-       "          [[-8.8412e-03, -1.3360e-02, -6.7431e-03],\n",
-       "           [-1.6904e-03, -3.3204e-03, -5.9615e-03],\n",
-       "           [-7.4343e-03, -4.9848e-03, -1.1525e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-1.8246e-04,  3.7305e-03,  5.9696e-03],\n",
-       "           [-3.7209e-03, -2.7809e-03, -4.1027e-03],\n",
-       "           [-9.7235e-03, -6.1212e-03, -1.0733e-02]],\n",
-       " \n",
-       "          [[-3.8010e-03,  1.8897e-03,  6.0774e-04],\n",
-       "           [ 5.7967e-03,  4.7659e-03,  6.4711e-03],\n",
-       "           [ 4.6535e-03,  1.3886e-02,  1.7228e-02]],\n",
-       " \n",
-       "          [[-5.9359e-03, -9.3137e-03, -2.3094e-02],\n",
-       "           [-5.6253e-03, -7.9942e-03, -1.2537e-02],\n",
-       "           [ 5.0757e-03,  5.8131e-03,  7.4299e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 7.4188e-03,  3.1704e-03,  1.2702e-02],\n",
-       "           [ 1.2121e-02,  8.9907e-03,  2.0869e-02],\n",
-       "           [-2.3000e-03, -6.4083e-03,  6.3872e-03]],\n",
-       " \n",
-       "          [[-9.6254e-03, -1.6525e-02, -1.7496e-02],\n",
-       "           [ 9.2063e-03,  8.0455e-03, -1.6826e-03],\n",
-       "           [ 2.1658e-02,  1.5988e-02,  1.3445e-02]],\n",
-       " \n",
-       "          [[ 1.3497e-02,  1.3803e-02,  1.1787e-02],\n",
-       "           [ 3.7837e-02,  4.0992e-02,  3.5181e-02],\n",
-       "           [ 4.0735e-02,  4.4689e-02,  3.4046e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-5.2921e-03, -7.2824e-03, -9.0411e-03],\n",
-       "           [ 1.4093e-03,  1.2995e-03,  3.6201e-03],\n",
-       "           [ 1.9119e-02,  1.6629e-02,  1.8864e-02]],\n",
-       " \n",
-       "          [[-5.0364e-03,  4.2296e-04,  5.7623e-04],\n",
-       "           [-1.1271e-02, -8.8127e-03, -6.7464e-03],\n",
-       "           [-1.9910e-02, -1.1922e-02, -1.1890e-02]],\n",
-       " \n",
-       "          [[-1.9070e-02, -1.2938e-02, -5.6206e-03],\n",
-       "           [-2.2706e-02, -1.7043e-02, -1.1125e-02],\n",
-       "           [-1.7253e-02, -1.6440e-02, -2.1782e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.1716e-02, -1.3335e-02, -1.2536e-02],\n",
-       "           [-3.2541e-03, -4.7157e-03, -5.3441e-03],\n",
-       "           [ 6.7110e-03,  5.0666e-03, -1.9457e-04]],\n",
-       " \n",
-       "          [[-2.0223e-03,  4.8749e-03, -1.2361e-02],\n",
-       "           [-8.7714e-04,  2.6614e-03, -1.5682e-02],\n",
-       "           [-1.2858e-03, -5.3473e-03, -1.5711e-02]],\n",
-       " \n",
-       "          [[-8.0510e-03, -1.1408e-02, -1.7233e-02],\n",
-       "           [-2.4991e-02, -3.4834e-02, -3.2081e-02],\n",
-       "           [-3.4814e-02, -2.9289e-02, -2.2612e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.6307e-02,  8.4615e-03,  8.3372e-03],\n",
-       "           [ 1.5255e-02,  1.1686e-02,  1.1058e-02],\n",
-       "           [-3.2079e-03, -2.8588e-05, -3.8627e-03]],\n",
-       " \n",
-       "          [[ 7.8471e-03,  1.4555e-02,  1.2405e-02],\n",
-       "           [ 1.0628e-02,  1.4652e-02,  1.5685e-02],\n",
-       "           [-2.2646e-03,  5.2729e-03,  8.6246e-03]],\n",
-       " \n",
-       "          [[ 4.8798e-03,  1.1522e-02,  1.0878e-02],\n",
-       "           [-1.3692e-04, -1.4222e-03,  1.0761e-03],\n",
-       "           [-1.2749e-02, -1.5229e-02, -8.0400e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.8610e-03,  1.1113e-03, -8.5017e-03],\n",
-       "           [-6.1634e-03, -5.3660e-03, -1.4284e-02],\n",
-       "           [-1.5827e-02, -2.7404e-02, -2.9903e-02]],\n",
-       " \n",
-       "          [[ 6.3719e-03,  5.0748e-03,  1.2066e-02],\n",
-       "           [-6.4401e-04,  1.2329e-02,  7.6772e-03],\n",
-       "           [-1.2538e-02, -1.4830e-02, -2.5548e-03]],\n",
-       " \n",
-       "          [[ 4.0490e-03, -3.0333e-03, -8.9996e-03],\n",
-       "           [-2.8229e-03, -3.5374e-03, -4.8557e-03],\n",
-       "           [-7.2337e-03, -6.9658e-03, -8.6928e-03]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.5994, 0.5959, 0.5208, 0.5810, 0.5483, 0.5433, 0.5267, 0.5624, 0.5711,\n",
-       "         0.6531, 0.5622, 0.6326, 0.5727, 0.5322, 0.5611, 0.5264, 0.5626, 0.6440,\n",
-       "         0.5609, 0.5753, 1.1522, 0.5599, 0.5733, 0.5845, 0.6387, 0.5495, 0.5808,\n",
-       "         0.5970, 0.6995, 0.5447, 0.8238, 0.6670, 0.5233, 0.5887, 0.5837, 0.7322,\n",
-       "         0.6055, 0.4953, 0.6271, 0.5546, 0.6331, 0.6304, 0.7197, 0.6890, 0.6073,\n",
-       "         0.5509, 0.5604, 0.7264, 0.5474, 0.4982, 0.6049, 0.9095, 0.6590, 0.5688,\n",
-       "         0.5585, 0.6740, 0.4547, 0.5417, 0.6212, 0.5447, 0.7588, 0.5408, 0.6884,\n",
-       "         0.7276, 0.5293, 0.6101, 0.5556, 0.4862, 0.5346, 0.4681, 0.6201, 0.5436,\n",
-       "         0.5487, 0.5864, 0.6614, 0.7490, 0.5932, 0.5776, 0.3258, 0.6589, 0.5574,\n",
-       "         0.6310, 0.5916, 0.6079, 0.6089, 0.5235, 0.6303, 0.5588, 0.5497, 0.5462,\n",
-       "         0.7271, 0.5477, 0.5922, 0.5834, 0.5250, 0.5262, 0.5996, 0.5911, 0.5818,\n",
-       "         0.6158, 0.6407, 0.7060, 0.5650, 0.6017, 0.6027, 0.6735, 0.6306, 0.6761,\n",
-       "         0.6291, 0.5449, 0.5855, 0.6511, 0.5578, 0.6801, 0.5346, 0.6599, 0.6883,\n",
-       "         0.6802, 0.7856, 0.6119, 0.6586, 0.5938, 0.5576, 0.6006, 0.6173, 0.6829,\n",
-       "         0.5436, 0.5250, 0.5981, 0.5653, 0.5326, 0.5834, 0.5748, 0.6329, 0.5600,\n",
-       "         0.5921, 0.6194, 0.5878, 0.6072, 0.5553, 0.6462, 0.7007, 0.4976, 0.6049,\n",
-       "         0.5939, 0.5832, 0.7288, 0.5683, 0.6759, 0.5691, 0.5739, 0.5938, 0.5068,\n",
-       "         0.6338, 0.5916, 0.6205, 0.6239, 0.5033, 0.6032, 0.5138, 0.6677, 0.5703,\n",
-       "         0.6024, 0.5551, 0.9148, 0.6634, 0.6563, 0.7050, 0.5136, 0.5128, 0.7067,\n",
-       "         0.6142, 0.6241, 0.5665, 0.5917, 0.6822, 0.6696, 0.5106, 0.5764, 0.6362,\n",
-       "         0.6028, 0.5904, 0.6516, 0.5520, 0.5373, 0.5490, 0.6186, 0.5339, 0.5722,\n",
-       "         0.6085, 0.6932, 0.5745, 0.5428, 0.6307, 0.5275, 0.6481, 0.5626, 0.5712,\n",
-       "         0.6062, 0.5761, 0.5568, 0.5602, 0.5348, 0.4930, 0.5731, 0.6777, 0.6202,\n",
-       "         0.6928, 0.5665, 0.5896, 0.6142, 0.5081, 0.5853, 0.7342, 0.4765, 0.5665,\n",
-       "         0.6680, 0.7576, 0.5013, 0.6047, 0.6317, 1.1198, 0.5027, 0.5405, 0.6140,\n",
-       "         0.5340, 0.6080, 0.5819, 0.7171, 0.5113, 0.5439, 0.4918, 0.4982, 0.7538,\n",
-       "         0.4517, 0.5190, 0.5568, 0.6484, 0.5593, 0.5768, 0.6094, 0.6365, 0.6161,\n",
-       "         0.5314, 0.5539, 0.5031, 0.6167, 0.5377, 0.5355, 0.5807, 0.5997, 0.6605,\n",
-       "         0.6015, 0.6642, 0.5623, 0.7135, 0.6264, 0.6663, 0.6029, 0.5005, 0.5575,\n",
-       "         0.6001, 0.5286, 0.5369, 0.5597, 1.2666, 0.5751, 0.7612, 0.5909, 0.6502,\n",
-       "         0.5956, 0.6035, 0.5539, 0.8987, 0.5989, 0.5544, 0.5940, 0.6597, 0.5491,\n",
-       "         0.5684, 0.6753, 0.6013, 0.5547, 0.6667, 1.0314, 0.6406, 1.2289, 0.4962,\n",
-       "         0.6138, 0.5767, 0.5931, 0.5831, 0.7591, 0.6420, 0.5790, 0.8140, 0.5170,\n",
-       "         0.5380, 0.7452, 0.5970, 0.6228, 0.6003, 0.5977, 0.5708, 0.5419, 0.6274,\n",
-       "         0.5505, 0.5620, 0.5960, 0.6507, 0.5666, 0.5782, 0.5185, 0.5875, 0.5721,\n",
-       "         0.6394, 0.6308, 0.6056, 0.5462, 0.5599, 0.7680, 0.6163, 0.6225, 0.8778,\n",
-       "         0.5049, 0.6160, 0.6629, 0.6327, 0.5526, 0.7304, 0.6787, 0.5729, 0.7237,\n",
-       "         0.6190, 0.6013, 0.6541, 0.5507, 0.6563, 0.6468, 0.4960, 0.5239, 0.4914,\n",
-       "         0.5048, 0.6216, 0.6109, 0.5377, 0.6106, 0.6888, 0.7945, 0.5816, 0.4580,\n",
-       "         0.5445, 0.6140, 0.4999, 0.5459, 0.5579, 0.6215, 0.4954, 0.6323, 0.5506,\n",
-       "         0.5807, 0.8080, 0.5613, 0.6082, 0.6365, 0.6743, 0.4960, 0.5637, 0.7304,\n",
-       "         0.6840, 0.5579, 0.6590, 0.6211, 0.6126, 0.5813, 0.5987, 0.5439, 0.5713,\n",
-       "         0.4998, 0.8206, 0.4980, 0.4733, 0.5912, 0.6222, 0.5254, 0.6074, 0.6120,\n",
-       "         0.6169, 0.6115, 0.6337, 0.6087, 0.5761, 0.6247, 0.5451, 0.4865, 0.5549,\n",
-       "         0.6119, 0.6170, 0.9921, 0.6076, 0.6103, 0.5529, 0.5838, 0.8223, 0.5551,\n",
-       "         0.5459, 0.5181, 0.5637, 0.6427, 0.6136, 0.5799, 0.5344, 0.5546, 0.5958,\n",
-       "         0.6626, 0.5769, 0.7248, 0.8100, 0.5961, 0.5800, 0.7165, 0.6544, 0.6231,\n",
-       "         0.6488, 0.5948, 0.5848, 0.5665, 0.5864, 0.6137, 0.8355, 0.5584, 0.5723,\n",
-       "         0.5782, 0.6123, 0.6292, 0.5538, 0.6686, 0.5943, 0.7209, 0.8445, 0.5450,\n",
-       "         0.5122, 0.5768, 0.5653, 0.5349, 0.4954, 0.6167, 0.5658, 0.6707, 0.5116,\n",
-       "         0.5832, 0.4740, 0.6135, 0.6129, 0.8014, 0.5990, 0.5833, 0.6414, 0.5431,\n",
-       "         0.6699, 0.7246, 0.6787, 0.5481, 0.7734, 0.6222, 0.5475, 0.7457, 0.6037,\n",
-       "         0.5542, 0.6730, 0.5869, 0.6600, 0.6325, 0.5994, 0.5507, 0.4746, 0.6490,\n",
-       "         0.5539, 0.5479, 0.7822, 0.6057, 0.5403, 0.6562, 0.8178, 0.6382, 0.7411,\n",
-       "         0.5893, 0.5842, 0.6971, 0.5672, 0.5586, 0.7194, 0.5803, 0.7049, 0.6462,\n",
-       "         0.6943, 0.5682, 0.8038, 0.4744, 0.6223, 0.5896, 0.5699, 0.6449, 0.5249,\n",
-       "         0.6200, 0.5969, 0.7734, 0.5503, 0.5536, 0.6162, 0.5980, 0.5234],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.1319, -0.1719, -0.1885, -0.1068, -0.1196, -0.1798, -0.1487, -0.1213,\n",
-       "         -0.1069, -0.1564, -0.1435, -0.1213, -0.0944, -0.1791, -0.1632, -0.0969,\n",
-       "         -0.0225, -0.1050, -0.1447, -0.2146,  0.3234, -0.1372, -0.0761, -0.1208,\n",
-       "         -0.1806, -0.2928, -0.1821, -0.2170, -0.1069, -0.1938,  0.0950, -0.0253,\n",
-       "         -0.1772, -0.1233, -0.1524, -0.0226, -0.1644, -0.1395, -0.1615, -0.1668,\n",
-       "         -0.2308, -0.1505, -0.1401, -0.1337, -0.1753, -0.1991, -0.2149,  0.0557,\n",
-       "         -0.0474, -0.1477, -0.1083, -0.0131, -0.2668, -0.0573, -0.1710, -0.2343,\n",
-       "         -0.1072, -0.0651, -0.2244, -0.2356, -0.0571, -0.0490, -0.0851, -0.1620,\n",
-       "         -0.1678, -0.2277, -0.1294, -0.1320, -0.1166, -0.1284, -0.1401, -0.0917,\n",
-       "         -0.1557, -0.1600, -0.1526,  0.0238, -0.1532, -0.0372, -0.0345, -0.1772,\n",
-       "         -0.0907, -0.0991, -0.0546, -0.1274, -0.1834, -0.2093, -0.0374, -0.1967,\n",
-       "         -0.0143, -0.2000,  0.1139, -0.1558,  0.0762, -0.1219, -0.1194, -0.1283,\n",
-       "         -0.0493, -0.1139, -0.0942, -0.1070, -0.1790, -0.2110, -0.2072, -0.1736,\n",
-       "         -0.1091, -0.1951, -0.1642, -0.1639, -0.1539, -0.1917, -0.1139, -0.1877,\n",
-       "         -0.0743, -0.1207, -0.1840, -0.1089, -0.1231, -0.1450, -0.0508, -0.0565,\n",
-       "         -0.1057, -0.1096, -0.1381, -0.1655, -0.1439, -0.1752, -0.1541, -0.0109,\n",
-       "         -0.2085, -0.2924, -0.1795, -0.1133, -0.1583, -0.1169, -0.0674, -0.1951,\n",
-       "         -0.0111, -0.0739, -0.1314, -0.2335, -0.0469, -0.0364, -0.1925, -0.1638,\n",
-       "         -0.1594, -0.1267,  0.0117, -0.1818, -0.0397, -0.1765, -0.1292, -0.1761,\n",
-       "         -0.1303,  0.0310, -0.1430, -0.1770, -0.0189, -0.0651, -0.0369, -0.1089,\n",
-       "          0.0216, -0.0733, -0.1499, -0.1884, -0.0678, -0.1435, -0.0580, -0.0487,\n",
-       "         -0.0952, -0.1788, -0.1465, -0.1879, -0.1576, -0.0796, -0.1099, -0.0245,\n",
-       "         -0.0430, -0.1416, -0.1719, -0.1813, -0.0928, -0.1963, -0.1952, -0.1203,\n",
-       "         -0.2142, -0.1384, -0.1552, -0.1366, -0.1843, -0.0770, -0.0470, -0.2117,\n",
-       "         -0.1533, -0.2109, -0.2010, -0.1012, -0.1595, -0.1131, -0.1465, -0.1094,\n",
-       "         -0.1169, -0.1415, -0.1467, -0.0906, -0.1194, -0.0141, -0.0384, -0.0950,\n",
-       "         -0.1168, -0.1044, -0.1797, -0.1793, -0.2425, -0.0926, -0.1253, -0.1659,\n",
-       "         -0.1817, -0.0706, -0.1180, -0.1289, -0.2338,  0.2704, -0.1694, -0.1754,\n",
-       "         -0.1667, -0.1367, -0.1648, -0.1194, -0.0954, -0.1428, -0.1596, -0.0964,\n",
-       "         -0.1009, -0.0115, -0.0517, -0.1590, -0.1541, -0.2430, -0.1491, -0.0896,\n",
-       "         -0.1472, -0.0443, -0.0455, -0.0783, -0.0750, -0.1675, -0.2089, -0.1161,\n",
-       "         -0.1112, -0.1152, -0.0569, -0.1655, -0.0933, -0.0436, -0.1291,  0.0207,\n",
-       "         -0.2126, -0.2269, -0.1510, -0.1892, -0.1606, -0.2055, -0.2050, -0.0644,\n",
-       "         -0.0742,  0.0447, -0.1499, -0.0993, -0.1123, -0.2547, -0.0826,  0.0225,\n",
-       "         -0.1795,  0.1321, -0.1632, -0.1782, -0.1357, -0.0316, -0.0996, -0.1726,\n",
-       "          0.0059, -0.2007, -0.1310, -0.1236,  0.0600, -0.1609,  0.1478, -0.1637,\n",
-       "         -0.1318, -0.2034, -0.1917, -0.1455, -0.0043, -0.0840, -0.1379, -0.1319,\n",
-       "         -0.1954, -0.1217, -0.2036, -0.1592, -0.1953, -0.0216, -0.0908, -0.1160,\n",
-       "         -0.1155, -0.1672, -0.2166, -0.1895, -0.1366, -0.0623, -0.1535, -0.0898,\n",
-       "         -0.1487, -0.1731, -0.1688, -0.2074, -0.1777, -0.1197, -0.1496, -0.0677,\n",
-       "         -0.0785, -0.2287, -0.1009, -0.0353, -0.0892, -0.1881, -0.0782, -0.1406,\n",
-       "         -0.1145, -0.0125,  0.0038, -0.1269, -0.1133, -0.1342, -0.1627, -0.1185,\n",
-       "         -0.2449, -0.1530, -0.1215, -0.0541, -0.1400, -0.0546, -0.1335, -0.2091,\n",
-       "         -0.1312, -0.1528, -0.1776, -0.0336,  0.0529, -0.1093, -0.1334, -0.1222,\n",
-       "         -0.0592, -0.1604, -0.1163, -0.1786, -0.1283, -0.1268, -0.1136, -0.1405,\n",
-       "         -0.1516,  0.0248, -0.1627, -0.1215, -0.1136, -0.1194, -0.1006, -0.1301,\n",
-       "         -0.0727, -0.2040, -0.1633, -0.0528, -0.0530, -0.1425, -0.1443, -0.1622,\n",
-       "         -0.1200, -0.0646, -0.1527, -0.0578, -0.1499, -0.1830, -0.1243, -0.1306,\n",
-       "          0.0116, -0.1062, -0.1167, -0.0834, -0.0338, -0.1713, -0.1713, -0.1876,\n",
-       "         -0.0932, -0.1582, -0.1209, -0.1412, -0.2104, -0.1960,  0.1892, -0.2083,\n",
-       "         -0.0712, -0.2242, -0.1739, -0.0868, -0.1264, -0.1308, -0.2915, -0.0876,\n",
-       "         -0.1169, -0.1873, -0.1550, -0.1562, -0.1523, -0.0930, -0.0650, -0.1881,\n",
-       "         -0.0928, -0.0321, -0.3400, -0.0370, -0.1523, -0.1561, -0.1728, -0.2111,\n",
-       "         -0.1385, -0.2597, -0.1474, -0.2189, -0.1571, -0.0121, -0.1609, -0.1592,\n",
-       "         -0.0987, -0.0996, -0.1923, -0.1490, -0.0821, -0.1308, -0.0886, -0.0707,\n",
-       "         -0.1256, -0.1426, -0.1202, -0.2459, -0.0790, -0.0476, -0.1055, -0.1284,\n",
-       "         -0.0705, -0.0635, -0.1041, -0.1693, -0.1385, -0.1173, -0.0453, -0.1722,\n",
-       "         -0.1398, -0.1581, -0.1906, -0.2279,  0.0036, -0.0747, -0.1395, -0.1791,\n",
-       "         -0.1504, -0.0855,  0.0237, -0.2644, -0.1841,  0.0165, -0.1787, -0.1283,\n",
-       "         -0.1300, -0.1123, -0.1269, -0.1589, -0.1787, -0.0206, -0.0864, -0.1110,\n",
-       "         -0.1223, -0.0546, -0.0943,  0.0567, -0.1774, -0.0175, -0.0763, -0.1865,\n",
-       "         -0.0134, -0.0297, -0.0911, -0.0115, -0.0626, -0.1252, -0.0159, -0.0858,\n",
-       "         -0.0988, -0.1653, -0.0926, -0.0826, -0.1020, -0.0737, -0.1397, -0.1240,\n",
-       "         -0.0881, -0.0420, -0.2007, -0.1328, -0.0558, -0.1485, -0.1676, -0.0500],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 1.7806e-02,  3.4412e-02,  2.7764e-02],\n",
-       "           [ 4.9379e-02,  6.1088e-02,  5.0636e-02],\n",
-       "           [ 9.7098e-03,  1.9061e-02,  1.6038e-02]],\n",
-       " \n",
-       "          [[-1.0722e-03, -1.0376e-03,  7.6440e-04],\n",
-       "           [-3.1922e-03, -6.5579e-03, -3.7090e-03],\n",
-       "           [ 2.0181e-02,  2.7989e-02,  2.1875e-02]],\n",
-       " \n",
-       "          [[-3.2819e-03, -9.3179e-04, -5.6302e-03],\n",
-       "           [ 4.4336e-03,  2.1798e-03, -2.5114e-03],\n",
-       "           [ 9.2456e-03,  6.1678e-03, -9.9494e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.8641e-02, -1.1169e-02, -1.5808e-02],\n",
-       "           [ 4.3150e-03,  3.6358e-03, -1.5571e-03],\n",
-       "           [ 3.4962e-02,  3.7736e-02,  3.0301e-02]],\n",
-       " \n",
-       "          [[ 4.1156e-02,  4.4048e-02,  3.5889e-02],\n",
-       "           [ 3.2188e-02,  2.5481e-02,  1.9367e-02],\n",
-       "           [ 2.2476e-02,  1.1954e-02,  1.7400e-02]],\n",
-       " \n",
-       "          [[ 1.3812e-02,  1.2737e-02,  7.3437e-03],\n",
-       "           [ 1.1189e-02,  6.0317e-03,  1.1258e-03],\n",
-       "           [-1.0572e-02, -7.1936e-03, -1.3010e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.7004e-02, -9.8847e-03, -2.1529e-02],\n",
-       "           [ 1.6752e-04,  6.4508e-03,  2.7410e-04],\n",
-       "           [-1.6065e-03, -1.0590e-03, -3.7610e-03]],\n",
-       " \n",
-       "          [[-1.6737e-02, -1.3340e-02, -7.6842e-03],\n",
-       "           [-2.2203e-02, -2.3720e-02, -1.7868e-02],\n",
-       "           [-1.8377e-02, -9.5497e-03, -9.1952e-03]],\n",
-       " \n",
-       "          [[-2.5114e-03,  6.2916e-03,  2.0791e-03],\n",
-       "           [-1.0368e-02, -7.5822e-03, -5.8671e-04],\n",
-       "           [-1.4898e-02, -2.6620e-02, -2.3197e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 7.6440e-03,  7.4965e-03,  2.9674e-03],\n",
-       "           [ 1.4980e-02,  2.1269e-02,  1.8560e-02],\n",
-       "           [ 1.1476e-02,  1.4881e-02,  6.3405e-03]],\n",
-       " \n",
-       "          [[ 1.0670e-03,  3.7063e-04, -7.3909e-03],\n",
-       "           [-2.6971e-03,  6.8509e-03,  1.9320e-03],\n",
-       "           [-8.7129e-03, -2.9034e-03, -6.4432e-03]],\n",
-       " \n",
-       "          [[-6.0921e-03, -6.2981e-03, -2.7957e-03],\n",
-       "           [-6.8928e-03, -9.6284e-03, -9.2898e-03],\n",
-       "           [-9.2725e-03, -5.4902e-03,  2.2746e-04]]],\n",
-       " \n",
-       " \n",
-       "         [[[-2.4574e-03, -2.3843e-02, -3.1548e-02],\n",
-       "           [ 7.2854e-03, -3.9882e-03, -6.5163e-03],\n",
-       "           [ 2.7049e-02,  1.5152e-02,  1.9180e-02]],\n",
-       " \n",
-       "          [[-1.6140e-02, -1.0570e-02, -9.0502e-03],\n",
-       "           [-2.1867e-02, -2.2596e-02, -1.9955e-02],\n",
-       "           [-1.8825e-02, -2.4299e-02, -1.9512e-02]],\n",
-       " \n",
-       "          [[ 1.7713e-02,  2.5403e-02,  8.8761e-03],\n",
-       "           [ 1.4482e-02,  1.5628e-02,  1.1739e-02],\n",
-       "           [ 7.8077e-04,  9.5521e-03,  1.2231e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 9.9940e-03,  1.8542e-02,  1.3690e-02],\n",
-       "           [ 7.2609e-03,  1.7275e-02,  1.1638e-02],\n",
-       "           [ 1.2801e-03,  1.1536e-02,  6.6781e-03]],\n",
-       " \n",
-       "          [[ 1.0567e-02,  1.8384e-02,  5.1952e-03],\n",
-       "           [ 1.4670e-02,  2.1017e-02,  3.4442e-03],\n",
-       "           [ 1.0579e-03,  1.7520e-02, -5.8190e-03]],\n",
-       " \n",
-       "          [[ 1.0571e-02,  1.5305e-02,  6.5996e-03],\n",
-       "           [ 1.0585e-02,  2.2530e-02,  7.9814e-03],\n",
-       "           [ 2.7877e-02,  3.9488e-02,  2.6896e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-1.3155e-02, -2.0943e-03, -2.7214e-03],\n",
-       "           [-2.5936e-03,  2.7762e-03, -1.0515e-03],\n",
-       "           [-3.0767e-03, -5.3123e-03, -1.0196e-02]],\n",
-       " \n",
-       "          [[-2.1022e-02, -1.0403e-02, -1.4251e-02],\n",
-       "           [-6.7688e-03,  1.2713e-02,  8.8978e-03],\n",
-       "           [ 1.5813e-03,  1.7962e-02,  1.0016e-02]],\n",
-       " \n",
-       "          [[ 8.3600e-03,  2.0357e-02, -7.5546e-03],\n",
-       "           [ 5.0324e-03, -5.2247e-03, -5.5377e-03],\n",
-       "           [ 3.0111e-03, -6.7034e-03, -1.0775e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.6780e-03, -9.1861e-03, -1.6231e-02],\n",
-       "           [ 1.3918e-02,  7.1538e-03, -3.1849e-03],\n",
-       "           [ 8.6204e-06, -2.0220e-03, -1.3531e-02]],\n",
-       " \n",
-       "          [[ 1.0243e-02,  7.8073e-03,  5.8363e-03],\n",
-       "           [ 1.5253e-02,  1.0230e-02,  8.6556e-03],\n",
-       "           [ 1.0900e-02,  1.1448e-02,  1.1873e-02]],\n",
-       " \n",
-       "          [[-7.1116e-03,  1.7481e-02,  1.4111e-02],\n",
-       "           [ 9.7643e-03,  2.8610e-02,  2.4662e-02],\n",
-       "           [-2.5571e-02, -1.0163e-02, -1.0075e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.0635e-02, -1.2532e-02, -7.8543e-03],\n",
-       "           [-1.8201e-02, -1.9619e-02, -2.2286e-02],\n",
-       "           [-7.1732e-03, -1.1469e-02, -1.3853e-02]],\n",
-       " \n",
-       "          [[-3.4978e-03, -6.9610e-03, -1.0916e-02],\n",
-       "           [-3.0160e-02, -3.2335e-02, -4.0828e-02],\n",
-       "           [-2.9336e-02, -3.3659e-02, -3.7803e-02]],\n",
-       " \n",
-       "          [[-7.0008e-03, -7.1164e-03,  2.4007e-03],\n",
-       "           [ 2.0166e-02,  9.2098e-03,  1.5621e-02],\n",
-       "           [ 1.7492e-02,  1.0153e-02,  9.3277e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.1391e-03,  1.8599e-02,  1.8185e-02],\n",
-       "           [-5.1287e-03, -6.6526e-03,  8.5423e-03],\n",
-       "           [-2.0835e-02, -2.1093e-02, -1.2472e-02]],\n",
-       " \n",
-       "          [[-8.1145e-03, -4.1409e-03,  7.8966e-03],\n",
-       "           [-3.1887e-02, -2.7453e-02, -1.7104e-02],\n",
-       "           [-3.9951e-02, -2.8723e-02, -2.4327e-02]],\n",
-       " \n",
-       "          [[ 1.0303e-03,  4.2297e-03, -8.5812e-04],\n",
-       "           [-1.4247e-02, -1.8428e-02, -1.8855e-02],\n",
-       "           [ 9.3211e-05, -1.5257e-02, -4.3301e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 5.1480e-03, -8.3759e-04,  2.8913e-03],\n",
-       "           [ 1.9867e-02,  1.9706e-02,  1.9475e-02],\n",
-       "           [ 1.6102e-02,  1.9030e-02,  1.0598e-02]],\n",
-       " \n",
-       "          [[ 2.0107e-03,  3.0594e-03,  2.9790e-03],\n",
-       "           [-1.5537e-02, -1.8503e-02, -1.6386e-02],\n",
-       "           [-1.6922e-02, -2.5556e-02, -1.5211e-02]],\n",
-       " \n",
-       "          [[-1.3637e-02, -2.2752e-02, -1.7073e-02],\n",
-       "           [-1.4189e-02, -2.3682e-02, -1.1697e-02],\n",
-       "           [-1.3626e-02, -1.6812e-02, -3.5310e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.6083e-02,  2.3046e-02,  2.9003e-02],\n",
-       "           [ 3.0720e-02,  4.3969e-02,  3.8252e-02],\n",
-       "           [ 5.5671e-03,  2.0102e-02,  9.5330e-03]],\n",
-       " \n",
-       "          [[-2.2159e-02, -3.2222e-02, -2.4900e-03],\n",
-       "           [-1.1650e-02, -2.3873e-02, -1.3772e-03],\n",
-       "           [ 4.4810e-03, -6.9048e-04,  1.2000e-02]],\n",
-       " \n",
-       "          [[-1.1230e-02, -9.6387e-03, -7.4568e-03],\n",
-       "           [-3.3318e-03, -4.4468e-03,  1.3826e-02],\n",
-       "           [-4.1722e-03, -2.4602e-03,  2.0446e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.2631, 0.2196, 0.2796, 0.2303, 0.2171, 0.2210, 0.2851, 0.2449, 0.2433,\n",
-       "         0.2673, 0.2351, 0.2496, 0.2629, 0.2540, 0.2155, 0.2323, 0.2280, 0.2461,\n",
-       "         0.2709, 0.2501, 0.2501, 0.2108, 0.2540, 0.2172, 0.2593, 0.2380, 0.2663,\n",
-       "         0.2537, 0.2607, 0.2350, 0.2404, 0.2908, 0.2392, 0.1944, 0.2736, 0.2749,\n",
-       "         0.2633, 0.1399, 0.1535, 0.3153, 0.2461, 0.2388, 0.2673, 0.2094, 0.1596,\n",
-       "         0.2384, 0.2555, 0.2110, 0.2528, 0.2414, 0.2805, 0.2522, 0.2266, 0.1621,\n",
-       "         0.2400, 0.2079, 0.2952, 0.2089, 0.2146, 0.2548, 0.1942, 0.2431, 0.2704,\n",
-       "         0.2567, 0.1688, 0.1862, 0.2151, 0.2359, 0.2402, 0.1473, 0.2540, 0.2403,\n",
-       "         0.2740, 0.2702, 0.2291, 0.2249, 0.2274, 0.2419, 0.2349, 0.2781, 0.2523,\n",
-       "         0.2628, 0.3348, 0.2876, 0.1875, 0.2602, 0.2617, 0.2507, 0.2235, 0.2050,\n",
-       "         0.2509, 0.2843, 0.2254, 0.2526, 0.2207, 0.2567, 0.3121, 0.2790, 0.2392,\n",
-       "         0.2367, 0.2287, 0.2199, 0.2442, 0.2477, 0.2564, 0.3344, 0.2710, 0.2380,\n",
-       "         0.2350, 0.2445, 0.2614, 0.2229, 0.1970, 0.2733, 0.2443, 0.2341, 0.2541,\n",
-       "         0.2050, 0.2233, 0.2323, 0.2333, 0.2556, 0.2028, 0.2785, 0.2442, 0.1832,\n",
-       "         0.2473, 0.2690, 0.2381, 0.2909, 0.2484, 0.2521, 0.2724, 0.2497, 0.2733,\n",
-       "         0.2349, 0.2501, 0.2458, 0.2354, 0.2331, 0.2396, 0.1965, 0.2211, 0.2712,\n",
-       "         0.2615, 0.1985, 0.2484, 0.2079, 0.2468, 0.2527, 0.3168, 0.2675, 0.2525,\n",
-       "         0.2769, 0.2491, 0.2656, 0.2196, 0.2443, 0.2697, 0.2278, 0.2640, 0.2507,\n",
-       "         0.2431, 0.2327, 0.2605, 0.2315, 0.2080, 0.2223, 0.2653, 0.2977, 0.2074,\n",
-       "         0.2446, 0.2283, 0.2264, 0.2764, 0.2246, 0.2667, 0.2347, 0.2522, 0.2672,\n",
-       "         0.2347, 0.2112, 0.2823, 0.2306, 0.2874, 0.2502, 0.2124, 0.2202, 0.1900,\n",
-       "         0.2295, 0.2399, 0.2720, 0.2622, 0.2810, 0.2166, 0.2528, 0.2427, 0.2612,\n",
-       "         0.2433, 0.2550, 0.2286, 0.2637, 0.2942, 0.2255, 0.2502, 0.2535, 0.2556,\n",
-       "         0.2674, 0.1599, 0.3068, 0.2166, 0.2564, 0.3213, 0.2737, 0.2549, 0.2300,\n",
-       "         0.2720, 0.2368, 0.2610, 0.2382, 0.2476, 0.2615, 0.2028, 0.2435, 0.1791,\n",
-       "         0.2542, 0.2831, 0.2108, 0.2065, 0.2680, 0.2784, 0.2475, 0.2283, 0.2316,\n",
-       "         0.2128, 0.2421, 0.3350, 0.2466, 0.2565, 0.1842, 0.2549, 0.2393, 0.2231,\n",
-       "         0.2160, 0.2597, 0.2983, 0.2538, 0.2040, 0.2588, 0.2205, 0.2207, 0.2658,\n",
-       "         0.1681, 0.2132, 0.2189, 0.2079, 0.2468, 0.2575, 0.2538, 0.2119, 0.2295,\n",
-       "         0.2438, 0.2712, 0.2253, 0.2417, 0.2645, 0.2532, 0.2495, 0.2007, 0.2511,\n",
-       "         0.2321, 0.1222, 0.1948, 0.2441, 0.2391, 0.2635, 0.2587, 0.2887, 0.2788,\n",
-       "         0.2591, 0.2503, 0.2368, 0.2832, 0.2171, 0.2181, 0.2639, 0.1301, 0.2204,\n",
-       "         0.2468, 0.2151, 0.2477, 0.2354, 0.2472, 0.2048, 0.2612, 0.1978, 0.2396,\n",
-       "         0.2627, 0.2555, 0.2281, 0.2377, 0.2185, 0.2788, 0.2301, 0.2254, 0.2200,\n",
-       "         0.2370, 0.2709, 0.2561, 0.2634, 0.2130, 0.2690, 0.2455, 0.2404, 0.2461,\n",
-       "         0.2694, 0.2370, 0.2801, 0.2322, 0.2529, 0.1947, 0.2936, 0.2599, 0.1387,\n",
-       "         0.2632, 0.2387, 0.2751, 0.2554, 0.2582, 0.2718, 0.1665, 0.2560, 0.2409,\n",
-       "         0.2619, 0.2607, 0.2447, 0.2528, 0.2579, 0.1995, 0.2503, 0.2596, 0.3012,\n",
-       "         0.1964, 0.2293, 0.2443, 0.2886, 0.2434, 0.2540, 0.2450, 0.2263, 0.3144,\n",
-       "         0.2889, 0.2814, 0.1508, 0.2335, 0.2368, 0.1916, 0.2503, 0.2510, 0.2852,\n",
-       "         0.1974, 0.2612, 0.2591, 0.2740, 0.2581, 0.2481, 0.2460, 0.2862, 0.2288,\n",
-       "         0.2548, 0.2708, 0.2430, 0.2357, 0.2216, 0.2585, 0.2369, 0.2660, 0.3111,\n",
-       "         0.2520, 0.2688, 0.2625, 0.2529, 0.2540, 0.1939, 0.2384, 0.2609, 0.2224,\n",
-       "         0.2161, 0.2488, 0.2444, 0.2302, 0.2201, 0.2663, 0.2401, 0.2494, 0.2102,\n",
-       "         0.2260, 0.2525, 0.2494, 0.3044, 0.2357, 0.2431, 0.2181, 0.1618, 0.2235,\n",
-       "         0.2690, 0.2176, 0.2824, 0.2607, 0.2147, 0.2414, 0.2277, 0.2558, 0.3176,\n",
-       "         0.2326, 0.2549, 0.2164, 0.1875, 0.2053, 0.2071, 0.1976, 0.2183, 0.2395,\n",
-       "         0.2606, 0.2415, 0.2687, 0.2401, 0.2480, 0.2603, 0.2370, 0.2344, 0.2688,\n",
-       "         0.2190, 0.1270, 0.2225, 0.2746, 0.2041, 0.2016, 0.2902, 0.2034, 0.2652,\n",
-       "         0.2570, 0.2886, 0.2321, 0.2901, 0.2613, 0.2156, 0.2527, 0.2462, 0.2632,\n",
-       "         0.2481, 0.2148, 0.2465, 0.2154, 0.2644, 0.2201, 0.2910, 0.2393, 0.1832,\n",
-       "         0.2469, 0.2739, 0.2486, 0.2614, 0.2409, 0.2572, 0.2653, 0.3180, 0.2348,\n",
-       "         0.2068, 0.2350, 0.2638, 0.2580, 0.2618, 0.2786, 0.2799, 0.2212, 0.2954,\n",
-       "         0.1553, 0.2333, 0.2377, 0.1880, 0.2579, 0.2925, 0.2645, 0.2367, 0.2699,\n",
-       "         0.2477, 0.1882, 0.2570, 0.2514, 0.2301, 0.2679, 0.2536, 0.2305, 0.2629,\n",
-       "         0.2672, 0.2287, 0.2268, 0.2212, 0.2798, 0.2582, 0.2970, 0.3271, 0.1898,\n",
-       "         0.2325, 0.2667, 0.2917, 0.2302, 0.2061, 0.2419, 0.2276, 0.2879],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.2125, -0.1714, -0.2698, -0.1439, -0.1656, -0.2087, -0.2470, -0.1789,\n",
-       "         -0.1016, -0.2279, -0.1542, -0.1636, -0.2394, -0.2531, -0.1028, -0.2061,\n",
-       "         -0.1826, -0.2396, -0.2612, -0.1775, -0.2428, -0.1674, -0.2283, -0.1299,\n",
-       "         -0.2282, -0.1667, -0.2822, -0.2461, -0.2518, -0.1591, -0.2144, -0.2751,\n",
-       "         -0.2064, -0.0962, -0.2288, -0.2364, -0.1873,  0.0187, -0.1275, -0.2762,\n",
-       "         -0.2335, -0.1585, -0.2267, -0.0822, -0.0866, -0.2124, -0.1494, -0.1638,\n",
-       "         -0.2378, -0.1805, -0.2253, -0.2048, -0.1345, -0.0611, -0.2134, -0.1853,\n",
-       "         -0.2462, -0.0836, -0.1730, -0.2152, -0.1558, -0.1778, -0.1861, -0.1887,\n",
-       "         -0.0692, -0.0766, -0.1552, -0.2255, -0.2004,  0.0655, -0.1839, -0.2151,\n",
-       "         -0.2440, -0.2293, -0.1758, -0.1914, -0.1606, -0.1991, -0.1740, -0.2824,\n",
-       "         -0.2155, -0.2320, -0.3447, -0.2745, -0.0500, -0.2500, -0.2255, -0.1861,\n",
-       "         -0.1963, -0.1080, -0.2485, -0.2196, -0.1357, -0.2253, -0.1261, -0.1798,\n",
-       "         -0.3012, -0.2450, -0.1828, -0.2174, -0.1691, -0.1669, -0.1683, -0.2023,\n",
-       "         -0.2142, -0.3890, -0.2717, -0.1765, -0.2090, -0.2277, -0.2641, -0.1816,\n",
-       "         -0.1073, -0.2372, -0.2140, -0.2093, -0.1944, -0.1460, -0.1610, -0.1768,\n",
-       "         -0.1651, -0.2108, -0.0989, -0.2511, -0.1872, -0.1602, -0.2185, -0.2536,\n",
-       "         -0.2396, -0.3152, -0.2705, -0.2364, -0.2463, -0.2289, -0.2061, -0.2179,\n",
-       "         -0.1856, -0.2594, -0.2054, -0.1824, -0.1798, -0.1282, -0.1279, -0.2511,\n",
-       "         -0.2236, -0.1870, -0.1932, -0.1268, -0.2192, -0.2001, -0.3905, -0.2801,\n",
-       "         -0.2129, -0.2274, -0.1573, -0.2302, -0.2025, -0.1971, -0.2306, -0.1722,\n",
-       "         -0.2163, -0.2341, -0.2052, -0.1550, -0.2473, -0.1887, -0.1092, -0.1658,\n",
-       "         -0.2826, -0.2688, -0.0749, -0.1673, -0.2353, -0.1800, -0.2943, -0.1925,\n",
-       "         -0.1956, -0.1570, -0.2179, -0.2377, -0.1926, -0.1085, -0.2910, -0.1724,\n",
-       "         -0.2499, -0.2278, -0.1281, -0.1788, -0.1018, -0.1932, -0.1919, -0.2144,\n",
-       "         -0.2279, -0.3078, -0.1644, -0.1838, -0.2008, -0.2033, -0.2267, -0.1894,\n",
-       "         -0.1751, -0.2513, -0.3062, -0.1076, -0.1588, -0.1776, -0.2409, -0.2730,\n",
-       "         -0.0473, -0.2965, -0.1114, -0.2062, -0.2672, -0.2549, -0.2377, -0.1592,\n",
-       "         -0.2096, -0.1253, -0.2349, -0.2022, -0.2313, -0.2244, -0.1790, -0.2029,\n",
-       "         -0.0732, -0.2200, -0.2344, -0.1223, -0.1133, -0.2307, -0.2976, -0.2406,\n",
-       "         -0.1759, -0.1613, -0.1498, -0.2109, -0.3565, -0.1915, -0.2266, -0.0910,\n",
-       "         -0.2054, -0.2055, -0.1546, -0.1482, -0.2047, -0.2508, -0.2258, -0.1559,\n",
-       "         -0.2427, -0.1925, -0.2018, -0.2413, -0.0464, -0.1394, -0.1789, -0.1489,\n",
-       "         -0.1805, -0.2227, -0.2566, -0.1809, -0.1763, -0.2019, -0.2105, -0.2044,\n",
-       "         -0.2176, -0.2344, -0.1946, -0.2225, -0.1072, -0.1925, -0.1181,  0.0601,\n",
-       "         -0.1220, -0.2144, -0.1934, -0.1885, -0.2151, -0.2566, -0.2745, -0.1699,\n",
-       "         -0.1840, -0.2021, -0.2005, -0.1392, -0.1555, -0.2364,  0.0159, -0.1828,\n",
-       "         -0.2151, -0.1592, -0.1955, -0.1930, -0.2324, -0.0807, -0.2091, -0.1148,\n",
-       "         -0.2511, -0.2395, -0.2347, -0.2112, -0.2008, -0.1596, -0.2520, -0.1496,\n",
-       "         -0.1558, -0.1492, -0.1515, -0.2450, -0.2289, -0.2623, -0.1288, -0.2015,\n",
-       "         -0.1800, -0.2092, -0.2316, -0.2323, -0.2124, -0.2595, -0.1694, -0.2067,\n",
-       "         -0.0893, -0.2424, -0.2376,  0.0085, -0.2220, -0.2161, -0.2724, -0.1936,\n",
-       "         -0.1841, -0.1906, -0.0733, -0.2393, -0.1850, -0.1805, -0.2555, -0.2275,\n",
-       "         -0.1926, -0.2185, -0.1496, -0.2056, -0.2383, -0.2973, -0.0455, -0.1702,\n",
-       "         -0.2295, -0.2491, -0.2441, -0.2111, -0.1987, -0.1941, -0.3506, -0.2548,\n",
-       "         -0.2545, -0.0651, -0.1338, -0.2048, -0.0857, -0.1933, -0.1413, -0.2384,\n",
-       "         -0.0751, -0.2223, -0.2139, -0.2508, -0.2033, -0.1893, -0.1731, -0.2511,\n",
-       "         -0.1978, -0.2081, -0.2139, -0.1687, -0.1901, -0.1606, -0.2085, -0.1823,\n",
-       "         -0.2225, -0.2762, -0.2042, -0.2336, -0.2592, -0.2059, -0.2144, -0.1122,\n",
-       "         -0.2044, -0.2196, -0.1609, -0.1774, -0.1899, -0.1567, -0.2037, -0.1894,\n",
-       "         -0.2909, -0.2255, -0.1572, -0.1314, -0.1795, -0.2162, -0.1328, -0.3491,\n",
-       "         -0.1946, -0.1461, -0.1343, -0.0372, -0.1324, -0.2373, -0.1768, -0.2286,\n",
-       "         -0.2392, -0.1535, -0.2114, -0.1716, -0.2119, -0.4101, -0.1535, -0.1954,\n",
-       "         -0.1514, -0.1071, -0.1527, -0.1222, -0.1518, -0.0912, -0.1853, -0.2688,\n",
-       "         -0.1760, -0.2097, -0.2268, -0.2136, -0.2154, -0.2067, -0.1896, -0.2935,\n",
-       "         -0.1497, -0.0264, -0.1846, -0.2715, -0.0965, -0.1335, -0.2921, -0.1384,\n",
-       "         -0.2657, -0.2256, -0.2703, -0.1369, -0.2745, -0.2081, -0.1409, -0.2530,\n",
-       "         -0.2280, -0.2331, -0.1926, -0.1762, -0.2131, -0.1810, -0.1963, -0.1246,\n",
-       "         -0.2737, -0.2035, -0.1310, -0.1479, -0.2117, -0.2390, -0.2397, -0.1827,\n",
-       "         -0.2044, -0.2181, -0.3145, -0.1731, -0.1163, -0.1673, -0.2266, -0.2307,\n",
-       "         -0.2054, -0.2424, -0.2663, -0.1503, -0.3190, -0.1272, -0.1975, -0.2133,\n",
-       "         -0.1132, -0.2293, -0.2525, -0.2094, -0.1634, -0.2562, -0.1875, -0.0401,\n",
-       "         -0.1820, -0.2250, -0.1891, -0.2636, -0.2027, -0.1004, -0.2176, -0.2051,\n",
-       "         -0.1508, -0.1652, -0.1165, -0.2437, -0.2580, -0.3250, -0.3718, -0.0128,\n",
-       "         -0.1624, -0.2053, -0.2356, -0.2059, -0.1514, -0.2032, -0.2061, -0.2664],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 0.0307,  0.0328,  0.0439],\n",
-       "           [ 0.0303,  0.0210,  0.0349],\n",
-       "           [ 0.0472,  0.0263,  0.0390]],\n",
-       " \n",
-       "          [[-0.0037,  0.0141,  0.0116],\n",
-       "           [ 0.0013,  0.0123,  0.0136],\n",
-       "           [ 0.0080,  0.0081,  0.0052]],\n",
-       " \n",
-       "          [[-0.0164, -0.0110, -0.0080],\n",
-       "           [-0.0131, -0.0125,  0.0002],\n",
-       "           [-0.0170, -0.0150, -0.0037]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0112,  0.0160,  0.0152],\n",
-       "           [ 0.0037,  0.0132,  0.0148],\n",
-       "           [ 0.0022,  0.0121,  0.0157]],\n",
-       " \n",
-       "          [[ 0.0037,  0.0154,  0.0260],\n",
-       "           [ 0.0038,  0.0143,  0.0184],\n",
-       "           [-0.0113,  0.0096,  0.0148]],\n",
-       " \n",
-       "          [[-0.0058, -0.0078, -0.0064],\n",
-       "           [ 0.0016,  0.0050,  0.0085],\n",
-       "           [-0.0036, -0.0081, -0.0100]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0097,  0.0080, -0.0044],\n",
-       "           [ 0.0101,  0.0086, -0.0021],\n",
-       "           [-0.0029, -0.0051, -0.0024]],\n",
-       " \n",
-       "          [[ 0.0003,  0.0040,  0.0025],\n",
-       "           [-0.0155, -0.0181, -0.0033],\n",
-       "           [-0.0083, -0.0108, -0.0022]],\n",
-       " \n",
-       "          [[-0.0142, -0.0127, -0.0038],\n",
-       "           [-0.0144, -0.0076, -0.0081],\n",
-       "           [-0.0076, -0.0043, -0.0102]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0137,  0.0097,  0.0211],\n",
-       "           [ 0.0174,  0.0060,  0.0131],\n",
-       "           [ 0.0214,  0.0175,  0.0271]],\n",
-       " \n",
-       "          [[-0.0199, -0.0165, -0.0143],\n",
-       "           [-0.0191, -0.0186, -0.0142],\n",
-       "           [-0.0149, -0.0191, -0.0148]],\n",
-       " \n",
-       "          [[ 0.0036,  0.0055,  0.0057],\n",
-       "           [ 0.0018,  0.0072,  0.0039],\n",
-       "           [ 0.0015,  0.0049,  0.0047]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0223, -0.0191, -0.0129],\n",
-       "           [-0.0008,  0.0010,  0.0021],\n",
-       "           [ 0.0099,  0.0130,  0.0108]],\n",
-       " \n",
-       "          [[ 0.0174,  0.0181,  0.0206],\n",
-       "           [ 0.0133,  0.0097,  0.0161],\n",
-       "           [ 0.0173,  0.0160,  0.0195]],\n",
-       " \n",
-       "          [[-0.0010, -0.0032, -0.0045],\n",
-       "           [ 0.0054,  0.0013, -0.0017],\n",
-       "           [ 0.0075,  0.0077,  0.0086]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0050, -0.0035, -0.0050],\n",
-       "           [-0.0078, -0.0074, -0.0075],\n",
-       "           [-0.0198, -0.0047, -0.0024]],\n",
-       " \n",
-       "          [[-0.0013, -0.0016,  0.0006],\n",
-       "           [ 0.0027,  0.0067,  0.0064],\n",
-       "           [-0.0044,  0.0085,  0.0114]],\n",
-       " \n",
-       "          [[-0.0106, -0.0138, -0.0182],\n",
-       "           [ 0.0014,  0.0039, -0.0069],\n",
-       "           [-0.0050,  0.0024, -0.0008]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-0.0123, -0.0099, -0.0072],\n",
-       "           [-0.0120, -0.0062,  0.0034],\n",
-       "           [-0.0068, -0.0014,  0.0082]],\n",
-       " \n",
-       "          [[-0.0102, -0.0081, -0.0068],\n",
-       "           [-0.0081, -0.0020, -0.0004],\n",
-       "           [-0.0050,  0.0065,  0.0081]],\n",
-       " \n",
-       "          [[-0.0065, -0.0019, -0.0106],\n",
-       "           [ 0.0007,  0.0080,  0.0009],\n",
-       "           [-0.0010,  0.0092,  0.0079]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0142,  0.0159,  0.0158],\n",
-       "           [ 0.0138,  0.0126,  0.0188],\n",
-       "           [ 0.0202,  0.0258,  0.0234]],\n",
-       " \n",
-       "          [[ 0.0019,  0.0062,  0.0028],\n",
-       "           [ 0.0035,  0.0057,  0.0027],\n",
-       "           [-0.0063, -0.0051, -0.0079]],\n",
-       " \n",
-       "          [[ 0.0151,  0.0117,  0.0196],\n",
-       "           [ 0.0186,  0.0148,  0.0171],\n",
-       "           [ 0.0198,  0.0107,  0.0170]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0195,  0.0290,  0.0342],\n",
-       "           [ 0.0195,  0.0253,  0.0278],\n",
-       "           [ 0.0253,  0.0343,  0.0319]],\n",
-       " \n",
-       "          [[ 0.0007,  0.0091,  0.0139],\n",
-       "           [-0.0029,  0.0039,  0.0091],\n",
-       "           [-0.0065, -0.0094,  0.0004]],\n",
-       " \n",
-       "          [[ 0.0008,  0.0054,  0.0117],\n",
-       "           [-0.0024, -0.0015,  0.0085],\n",
-       "           [ 0.0064,  0.0026, -0.0004]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0122,  0.0164,  0.0152],\n",
-       "           [ 0.0131,  0.0151,  0.0163],\n",
-       "           [ 0.0117,  0.0154,  0.0110]],\n",
-       " \n",
-       "          [[-0.0090, -0.0074, -0.0060],\n",
-       "           [-0.0025, -0.0029, -0.0109],\n",
-       "           [-0.0060, -0.0108, -0.0174]],\n",
-       " \n",
-       "          [[-0.0171, -0.0097, -0.0156],\n",
-       "           [-0.0034, -0.0007, -0.0042],\n",
-       "           [-0.0021, -0.0090, -0.0105]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0022,  0.0029,  0.0065],\n",
-       "           [ 0.0034,  0.0140,  0.0111],\n",
-       "           [-0.0004,  0.0172,  0.0092]],\n",
-       " \n",
-       "          [[ 0.0135, -0.0018, -0.0039],\n",
-       "           [ 0.0145, -0.0010, -0.0047],\n",
-       "           [ 0.0049, -0.0103, -0.0114]],\n",
-       " \n",
-       "          [[ 0.0079,  0.0049,  0.0029],\n",
-       "           [ 0.0061,  0.0060,  0.0039],\n",
-       "           [ 0.0072,  0.0051,  0.0024]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0050,  0.0132,  0.0179],\n",
-       "           [ 0.0041,  0.0048,  0.0107],\n",
-       "           [ 0.0168,  0.0085,  0.0156]],\n",
-       " \n",
-       "          [[-0.0297, -0.0254, -0.0250],\n",
-       "           [-0.0315, -0.0252, -0.0250],\n",
-       "           [-0.0324, -0.0310, -0.0281]],\n",
-       " \n",
-       "          [[ 0.0155,  0.0217,  0.0193],\n",
-       "           [ 0.0138,  0.0119,  0.0162],\n",
-       "           [ 0.0162,  0.0153,  0.0154]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([1.4267, 1.5260, 1.5765, 1.2969, 1.2852, 1.5146, 1.4977, 1.3114, 1.4437,\n",
-       "         1.4238, 1.2671, 1.3311, 1.4422, 1.4233, 1.3574, 1.4328, 1.1302, 1.2859,\n",
-       "         1.3439, 1.4388, 1.0789, 1.3220, 1.3338, 1.2141, 1.4690, 1.5821, 1.3880,\n",
-       "         1.5576, 1.3979, 1.4905, 1.3473, 1.3621, 1.3447, 1.3405, 1.3118, 1.3942,\n",
-       "         1.2924, 1.3881, 1.4966, 1.4181, 1.4606, 1.3812, 1.3722, 1.3674, 1.4398,\n",
-       "         1.4378, 1.4863, 1.1296, 1.3318, 1.3951, 1.3623, 1.3381, 1.3492, 1.3986,\n",
-       "         1.3125, 1.4245, 1.3394, 1.2450, 1.5234, 1.4323, 1.4687, 1.4532, 1.2551,\n",
-       "         1.2517, 1.3771, 1.4633, 1.3385, 1.3437, 1.3450, 1.4195, 1.4493, 1.3256,\n",
-       "         1.3526, 1.4292, 1.5444, 1.3065, 1.3506, 1.2936, 1.9072, 1.3554, 1.4220,\n",
-       "         1.4178, 1.3294, 1.1712, 1.3057, 1.3938, 1.2535, 1.4204, 1.2341, 1.4833,\n",
-       "         1.5371, 1.3257, 1.0280, 1.3247, 1.2943, 1.4956, 1.3470, 1.3236, 1.2515,\n",
-       "         1.4094, 1.1999, 1.5091, 1.4949, 1.3819, 1.2488, 1.4039, 1.4940, 1.4105,\n",
-       "         1.4988, 1.5488, 1.5227, 1.4619, 1.3685, 1.2628, 1.4182, 1.3017, 1.4530,\n",
-       "         1.3363, 1.3500, 1.1785, 1.4065, 1.3061, 1.5020, 1.3846, 1.3979, 1.4838,\n",
-       "         1.3423, 1.2809, 1.5095, 1.5280, 1.3803, 1.2880, 1.4618, 1.4343, 1.3476,\n",
-       "         1.4508, 1.3792, 1.2939, 1.3660, 1.4474, 1.3703, 1.4636, 1.5069, 1.4837,\n",
-       "         1.4698, 1.3232, 1.3714, 1.5595, 1.2711, 1.4614, 1.3339, 1.3077, 1.4082,\n",
-       "         1.2007, 1.4259, 1.4183, 1.3312, 1.3698, 1.2555, 1.2302, 1.1898, 1.2009,\n",
-       "         1.4985, 1.3771, 1.7319, 1.4310, 1.3628, 1.3921, 1.2663, 1.3978, 1.3909,\n",
-       "         1.3979, 1.4989, 1.3313, 1.3860, 1.3876, 1.2406, 1.2852, 1.4415, 1.4973,\n",
-       "         1.4135, 1.4677, 1.4161, 1.2719, 1.5440, 1.3615, 1.4012, 1.2899, 1.4372,\n",
-       "         1.3594, 1.3184, 1.4460, 1.4251, 1.4728, 1.4062, 1.2542, 1.3125, 1.3195,\n",
-       "         1.3433, 1.3796, 1.3112, 1.4887, 1.4805, 1.2647, 1.3114, 1.2972, 1.3623,\n",
-       "         1.4377, 1.5346, 1.4157, 1.5425, 1.4330, 1.4717, 1.4607, 1.3312, 1.4353,\n",
-       "         1.3795, 1.3640, 1.3391, 1.4844, 1.4820, 1.3246, 1.3414, 1.3771, 1.3991,\n",
-       "         1.3519, 1.4974, 1.4848, 1.4073, 1.3879, 1.3147, 1.2681, 1.3048, 1.4930,\n",
-       "         1.3080, 1.5286, 1.3866, 1.5600, 1.2956, 1.3757, 1.2831, 1.3818, 1.2154,\n",
-       "         1.2892, 1.2486, 1.4036, 1.4746, 1.2342, 1.3532, 1.2670, 1.2771, 1.5058,\n",
-       "         1.3629, 1.3772, 1.4998, 1.3030, 1.4407, 1.5745, 1.2903, 1.3678, 1.3774,\n",
-       "         1.3273, 1.3199, 1.3960, 1.1733, 1.4757, 1.5131, 1.2943, 1.4804, 1.5082,\n",
-       "         1.2187, 1.3735, 1.3853, 1.3920, 1.4322, 1.4420, 1.4136, 1.3275, 1.5234,\n",
-       "         1.4647, 1.3838, 1.3756, 1.3011, 1.3125, 1.2718, 1.4858, 1.3642, 1.3389,\n",
-       "         1.3649, 1.3757, 1.4592, 1.2728, 1.4220, 1.2572, 1.3289, 1.6292, 1.5737,\n",
-       "         1.4750, 1.3725, 1.4807, 1.4702, 1.3282, 1.2596, 1.3990, 1.4105, 1.3240,\n",
-       "         1.4900, 1.4978, 1.4661, 1.1720, 1.3644, 1.4576, 1.4800, 1.5051, 1.4588,\n",
-       "         1.5418, 1.4920, 1.4438, 1.2301, 1.1575, 1.4535, 1.5264, 1.4926, 1.3228,\n",
-       "         1.3420, 1.4614, 1.2434, 1.3137, 1.3765, 1.3785, 1.3239, 1.4535, 1.3416,\n",
-       "         1.3825, 1.4821, 1.4058, 1.5057, 1.3625, 1.4626, 1.2722, 1.3717, 1.2478,\n",
-       "         1.4184, 1.4249, 1.1326, 1.3551, 1.3911, 1.2960, 1.5206, 1.2529, 1.3490,\n",
-       "         1.4758, 1.2089, 1.3833, 1.3635, 1.3139, 1.3299, 1.3632, 1.2911, 1.3745,\n",
-       "         1.6052, 1.2451, 1.4480, 1.2393, 1.4221, 1.2618, 1.4080, 1.4253, 1.2613,\n",
-       "         1.4979, 1.4523, 1.3284, 1.2330, 1.4543, 1.3199, 1.4995, 1.4915, 1.3269,\n",
-       "         1.4501, 1.4885, 1.3683, 1.2631, 1.4193, 1.3885, 1.3825, 1.5311, 1.2464,\n",
-       "         1.2251, 1.2754, 1.5096, 1.3713, 1.5511, 1.3670, 1.4443, 1.3641, 1.5768,\n",
-       "         1.4858, 1.3989, 1.7184, 1.4602, 1.4273, 1.3636, 1.5034, 1.4430, 1.3176,\n",
-       "         1.3161, 1.4630, 1.1956, 1.3948, 1.3010, 1.2901, 1.4553, 1.5046, 1.2893,\n",
-       "         1.2124, 1.6199, 1.3447, 1.3798, 1.5739, 1.2244, 1.1869, 1.5606, 1.4657,\n",
-       "         1.4809, 1.4677, 1.6331, 1.2783, 1.4212, 1.3310, 1.6360, 1.2949, 1.3562,\n",
-       "         1.3088, 1.3197, 1.4303, 1.3250, 1.4417, 1.4175, 1.5384, 1.3519, 1.3359,\n",
-       "         1.3650, 1.3920, 1.5038, 1.4414, 1.6704, 1.3346, 1.2865, 1.4081, 1.3630,\n",
-       "         1.4332, 1.4360, 1.4070, 1.1556, 1.3467, 1.4193, 1.2466, 1.2726, 1.4950,\n",
-       "         1.3867, 1.4732, 1.4715, 1.4221, 1.3179, 1.3446, 1.4469, 1.0591, 1.3963,\n",
-       "         1.4550, 1.2807, 1.4452, 1.4522, 1.2983, 1.4143, 1.4210, 1.3362, 1.5077,\n",
-       "         1.3266, 1.4070, 1.3770, 1.3893, 1.2065, 1.2552, 1.5297, 1.4298, 1.3058,\n",
-       "         1.2551, 1.4403, 1.5755, 1.3325, 1.2546, 1.3087, 1.3732, 1.6142, 1.4213,\n",
-       "         1.3655, 1.3360, 1.2816, 1.2521, 1.3337, 1.3020, 1.4251, 1.3405, 1.3290,\n",
-       "         1.2571, 1.3717, 1.2922, 1.3501, 1.3596, 1.5325, 1.4784, 1.2530],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.1167,  0.1316,  0.1894,  0.1230,  0.1001,  0.1484,  0.1454,  0.1426,\n",
-       "          0.2056,  0.1397,  0.0914,  0.1442,  0.1177,  0.1004,  0.1092,  0.2496,\n",
-       "          0.1782,  0.1402,  0.0892,  0.1465, -0.0252,  0.0924,  0.1443,  0.1121,\n",
-       "          0.0918,  0.0830,  0.1200,  0.0916,  0.1483,  0.1463,  0.0662,  0.1196,\n",
-       "          0.1086,  0.1407,  0.0792,  0.1563,  0.1454,  0.1219,  0.1185,  0.1268,\n",
-       "          0.0681,  0.1051,  0.1127,  0.1583,  0.0659,  0.1630,  0.1370,  0.1467,\n",
-       "          0.1247,  0.1020,  0.1379,  0.2230,  0.1023,  0.1515,  0.0672,  0.0801,\n",
-       "          0.1509,  0.1573,  0.1789,  0.1135,  0.1366,  0.2067,  0.1244,  0.1450,\n",
-       "          0.1178,  0.0864,  0.1022,  0.1331,  0.1473,  0.1330,  0.1876,  0.1533,\n",
-       "          0.1496,  0.0734,  0.1487,  0.1743,  0.1371,  0.1378,  0.0797,  0.1178,\n",
-       "          0.1546,  0.1760,  0.1583,  0.0614,  0.1124,  0.1656,  0.2125,  0.0955,\n",
-       "          0.0959,  0.0693,  0.3650,  0.1385,  0.1302,  0.1884,  0.1348,  0.2058,\n",
-       "          0.1515,  0.1247,  0.1430,  0.1675,  0.1533,  0.0767,  0.1444,  0.1008,\n",
-       "          0.0729,  0.1200,  0.1659,  0.0790,  0.1044,  0.1324,  0.1305,  0.0758,\n",
-       "          0.2084,  0.1057,  0.1106,  0.0880,  0.1650,  0.0631,  0.1651,  0.1768,\n",
-       "          0.1924,  0.1351,  0.1484,  0.1218,  0.0785,  0.0555,  0.1410,  0.2106,\n",
-       "          0.0492,  0.0928,  0.0778,  0.1494,  0.0892,  0.1633,  0.1657,  0.0747,\n",
-       "          0.2005,  0.1397,  0.1776,  0.0817,  0.1623,  0.1458,  0.1285,  0.1248,\n",
-       "          0.1525,  0.1265,  0.2453,  0.1597,  0.1549,  0.1037,  0.1268,  0.1259,\n",
-       "          0.1089,  0.1760,  0.1542,  0.1044,  0.1433,  0.1780,  0.1426,  0.1472,\n",
-       "          0.1836,  0.1655,  0.1369,  0.1233,  0.3612,  0.1232,  0.1781,  0.2348,\n",
-       "          0.1145,  0.1118,  0.1206,  0.1172,  0.1383,  0.1261,  0.1734,  0.1761,\n",
-       "          0.1902,  0.1189,  0.1003,  0.1119,  0.0984,  0.1247,  0.1267,  0.1361,\n",
-       "          0.1198,  0.1172,  0.1155,  0.0788,  0.1231,  0.1491,  0.2238,  0.1660,\n",
-       "          0.1244,  0.1064,  0.0843,  0.1317,  0.1197,  0.0703,  0.1307,  0.1078,\n",
-       "          0.1242,  0.1605,  0.1189,  0.1244,  0.0509,  0.1781,  0.1609,  0.1075,\n",
-       "          0.1407,  0.1565,  0.1078,  0.1150,  0.0881,  0.1624,  0.1004,  0.1089,\n",
-       "          0.0961,  0.1388,  0.1075,  0.0956,  0.1714, -0.0287,  0.1424,  0.1151,\n",
-       "          0.1566,  0.1146,  0.1049,  0.1386,  0.1524,  0.1361,  0.1137,  0.1349,\n",
-       "          0.1106,  0.1657,  0.1643,  0.1266,  0.1870,  0.0829,  0.1273,  0.1414,\n",
-       "          0.1503,  0.2325,  0.2394,  0.1418,  0.1299,  0.0772,  0.0745,  0.1223,\n",
-       "          0.1370,  0.1334,  0.1862,  0.1302,  0.1674,  0.2044,  0.0896,  0.1966,\n",
-       "          0.1222,  0.0803,  0.1325,  0.0623,  0.1879,  0.1306,  0.1492,  0.1262,\n",
-       "          0.1300, -0.0931,  0.1556,  0.1484,  0.1509,  0.1176,  0.1144,  0.1696,\n",
-       "          0.1336,  0.1813,  0.1104,  0.0951,  0.1240,  0.1682,  0.1964,  0.0689,\n",
-       "          0.2040,  0.0968,  0.1086,  0.0826,  0.0382,  0.1289, -0.0789,  0.1022,\n",
-       "          0.0851,  0.0827,  0.0889,  0.0945,  0.1840,  0.1195,  0.1377,  0.1385,\n",
-       "          0.1368,  0.1338,  0.0558,  0.1030,  0.1287,  0.1516,  0.1366,  0.1079,\n",
-       "          0.1605,  0.1027,  0.0741,  0.0789,  0.1572,  0.1552,  0.1100,  0.1901,\n",
-       "          0.1077,  0.1377,  0.1465,  0.0989,  0.1529,  0.0979,  0.1444,  0.1623,\n",
-       "          0.2104,  0.1167,  0.1736,  0.0686,  0.1452,  0.0830,  0.1571,  0.1242,\n",
-       "          0.1425,  0.2167,  0.2072,  0.1613,  0.0921,  0.1150,  0.1242,  0.0630,\n",
-       "          0.1439,  0.1411,  0.1689,  0.1692,  0.1770,  0.1690,  0.1054,  0.0427,\n",
-       "          0.1290,  0.1515,  0.1915,  0.2119,  0.2632,  0.1501,  0.1275,  0.1702,\n",
-       "          0.2082,  0.0850,  0.1391,  0.0838,  0.1137,  0.1366,  0.1117,  0.1751,\n",
-       "          0.1699,  0.1994,  0.0857,  0.1467,  0.0787,  0.1488,  0.1417,  0.1072,\n",
-       "          0.0892,  0.1193,  0.1362,  0.1342,  0.1254,  0.0943,  0.1596,  0.1296,\n",
-       "          0.1338,  0.1504,  0.0971,  0.1343,  0.1300,  0.0819,  0.1315,  0.1265,\n",
-       "          0.1863,  0.1177,  0.1588,  0.1250,  0.1697,  0.1079,  0.0908,  0.1185,\n",
-       "          0.1779,  0.0942,  0.1625,  0.1432,  0.0622,  0.1309,  0.3094,  0.1013,\n",
-       "          0.1384,  0.0253,  0.1257,  0.1354,  0.1296,  0.1092,  0.1039,  0.1559,\n",
-       "          0.1099,  0.1001,  0.1069,  0.1344,  0.1230,  0.0884,  0.1787,  0.0933,\n",
-       "          0.1464,  0.2444,  0.0713,  0.1701,  0.0246,  0.1255,  0.1410,  0.0901,\n",
-       "          0.1050,  0.0672,  0.1049,  0.0995,  0.1064,  0.2897,  0.0691,  0.0738,\n",
-       "          0.1612,  0.1474,  0.1203,  0.0897,  0.1074,  0.1581,  0.1550,  0.0383,\n",
-       "          0.1592,  0.0660,  0.1279,  0.2071,  0.1229,  0.1399,  0.1317,  0.1769,\n",
-       "          0.2240,  0.1395,  0.1672,  0.0939,  0.1334,  0.1325,  0.1883,  0.1431,\n",
-       "          0.1119,  0.0952,  0.0893,  0.0912,  0.2066,  0.2172,  0.0871,  0.1057,\n",
-       "          0.1636,  0.1478,  0.0805,  0.1497,  0.1245,  0.2393,  0.1150,  0.1274,\n",
-       "          0.1214,  0.0910,  0.1234,  0.0789,  0.1455,  0.1999,  0.1511,  0.1795,\n",
-       "          0.1525,  0.1061,  0.1410,  0.2661,  0.1086,  0.1224,  0.1564,  0.0725,\n",
-       "          0.2078,  0.1733,  0.0966,  0.2133,  0.1860,  0.2328,  0.2395,  0.1772,\n",
-       "          0.0997,  0.0625,  0.1326,  0.1630,  0.1030,  0.1536,  0.1383,  0.1475,\n",
-       "          0.1367,  0.1252,  0.0528,  0.1083,  0.1886,  0.1151,  0.0719,  0.1743],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-7.0346e-03, -1.1870e-02, -1.7352e-02],\n",
-       "           [ 1.6149e-02, -1.0473e-02, -1.7529e-02],\n",
-       "           [-1.5558e-02, -8.7468e-03, -3.6376e-02]],\n",
-       " \n",
-       "          [[-3.4681e-02, -1.8366e-02, -2.2701e-02],\n",
-       "           [ 7.5499e-03,  1.0328e-02, -1.8652e-02],\n",
-       "           [-6.0877e-03, -4.8917e-04, -1.5877e-02]],\n",
-       " \n",
-       "          [[ 2.3373e-02,  1.8373e-02,  2.2226e-02],\n",
-       "           [ 3.0777e-02, -1.7654e-02, -2.0565e-03],\n",
-       "           [ 1.5562e-02,  3.4506e-03,  2.8517e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 3.4214e-02,  1.4186e-02,  6.3066e-03],\n",
-       "           [ 1.6900e-02, -6.7811e-03, -3.1465e-02],\n",
-       "           [-2.1439e-02, -1.9850e-02,  2.8199e-02]],\n",
-       " \n",
-       "          [[ 3.4352e-03,  9.6688e-03,  1.7032e-02],\n",
-       "           [ 1.9644e-02, -6.1005e-03, -2.8700e-02],\n",
-       "           [-5.4266e-03, -1.0961e-02, -1.2901e-02]],\n",
-       " \n",
-       "          [[ 9.5945e-03, -3.0505e-02,  4.5461e-03],\n",
-       "           [-5.2719e-05,  3.0392e-02,  2.2864e-02],\n",
-       "           [ 1.5262e-02,  1.3040e-02,  1.4098e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.9679e-02,  2.0914e-02, -1.0105e-02],\n",
-       "           [-2.5025e-02, -2.1146e-02,  1.2246e-02],\n",
-       "           [-1.4500e-02, -1.9731e-03,  1.9754e-03]],\n",
-       " \n",
-       "          [[-6.0858e-03,  1.4813e-02,  2.6509e-02],\n",
-       "           [-2.7528e-02, -1.9550e-02, -3.3897e-02],\n",
-       "           [ 1.2082e-02, -2.1081e-02, -6.4246e-03]],\n",
-       " \n",
-       "          [[-8.5567e-03, -7.3452e-03, -4.9434e-03],\n",
-       "           [-1.1883e-02, -2.4322e-02, -1.6240e-02],\n",
-       "           [-2.7913e-02,  1.3324e-02,  1.6214e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.0357e-02,  2.4563e-02,  8.7550e-03],\n",
-       "           [ 3.0576e-02,  2.9696e-02,  9.0418e-03],\n",
-       "           [-1.5374e-02,  3.1216e-03, -1.8267e-02]],\n",
-       " \n",
-       "          [[ 2.6431e-02, -3.7162e-03,  1.1652e-02],\n",
-       "           [-7.1331e-03, -2.0171e-03, -9.5339e-03],\n",
-       "           [-1.6084e-02,  4.3867e-03, -1.2066e-02]],\n",
-       " \n",
-       "          [[ 2.4524e-02,  2.4730e-02,  3.8379e-02],\n",
-       "           [-1.2325e-02,  7.0312e-03, -2.7973e-02],\n",
-       "           [-1.5948e-02, -4.0324e-02, -3.1229e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-1.1753e-02, -1.1758e-02,  5.5450e-03],\n",
-       "           [ 3.1368e-02,  1.1105e-02,  2.4132e-02],\n",
-       "           [-6.1549e-03,  1.7371e-03,  2.9668e-02]],\n",
-       " \n",
-       "          [[ 2.0085e-02, -8.0775e-03,  7.9938e-03],\n",
-       "           [-2.0565e-03,  5.3338e-03, -1.7842e-02],\n",
-       "           [ 2.2668e-02, -2.2085e-02,  1.4212e-02]],\n",
-       " \n",
-       "          [[ 1.1579e-02,  2.6819e-02, -2.7885e-02],\n",
-       "           [ 1.6626e-02,  2.9596e-02, -1.3447e-02],\n",
-       "           [ 2.8547e-02, -9.1630e-03, -1.3123e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.2430e-03,  1.0173e-02, -9.8676e-03],\n",
-       "           [-1.3133e-02, -3.0982e-02, -1.9675e-02],\n",
-       "           [ 2.3474e-02,  1.6410e-02, -9.7469e-03]],\n",
-       " \n",
-       "          [[-9.5466e-03, -1.7606e-02,  8.8069e-03],\n",
-       "           [ 7.1528e-03,  3.1795e-02, -2.6513e-02],\n",
-       "           [-1.0580e-02, -1.7203e-02,  9.6903e-04]],\n",
-       " \n",
-       "          [[-6.3921e-03, -1.7054e-03, -1.9820e-02],\n",
-       "           [ 2.7150e-02,  2.4651e-02,  2.9701e-02],\n",
-       "           [ 9.5194e-03,  3.0919e-02, -2.0004e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-2.1441e-02,  1.8857e-02, -4.6939e-05],\n",
-       "           [ 2.5659e-02,  3.1500e-02, -2.4843e-02],\n",
-       "           [-1.6371e-03, -1.2205e-02, -1.4026e-02]],\n",
-       " \n",
-       "          [[ 1.9208e-03, -4.6840e-03,  1.3337e-02],\n",
-       "           [ 1.1550e-02,  1.0350e-02, -9.7073e-03],\n",
-       "           [ 2.0749e-02,  6.4489e-03, -2.1855e-02]],\n",
-       " \n",
-       "          [[ 1.6373e-02,  1.6755e-02,  1.7957e-02],\n",
-       "           [ 1.1983e-02, -1.0099e-02, -1.9602e-02],\n",
-       "           [-1.0700e-02,  7.1844e-03,  2.8502e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.4301e-02,  7.2468e-03, -1.1483e-02],\n",
-       "           [ 2.9111e-02,  4.1964e-03,  2.3843e-02],\n",
-       "           [ 2.2523e-02, -2.1604e-02,  3.5255e-02]],\n",
-       " \n",
-       "          [[-5.0159e-03, -1.6742e-02, -1.2177e-02],\n",
-       "           [-2.6985e-02,  9.4163e-03, -1.0180e-02],\n",
-       "           [ 8.7551e-03, -1.3563e-02,  2.0977e-02]],\n",
-       " \n",
-       "          [[-2.0590e-02, -1.7352e-02, -3.2940e-02],\n",
-       "           [-1.4589e-02, -1.1868e-02,  1.2304e-02],\n",
-       "           [-3.9509e-02,  3.5992e-03, -1.0020e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.9046e-02, -3.4917e-02, -1.4280e-02],\n",
-       "           [-2.9557e-02,  1.8198e-02,  5.8623e-04],\n",
-       "           [ 1.6323e-03, -1.2327e-02, -1.9174e-02]],\n",
-       " \n",
-       "          [[ 1.2784e-02, -2.3866e-02,  6.2024e-03],\n",
-       "           [ 1.9748e-02, -1.1189e-03, -2.8169e-02],\n",
-       "           [-2.8891e-02, -2.3239e-02,  1.2427e-02]],\n",
-       " \n",
-       "          [[-3.5139e-02,  1.7911e-02,  4.2646e-03],\n",
-       "           [-2.6214e-03, -3.2832e-02,  1.9012e-02],\n",
-       "           [-1.6886e-02, -2.7754e-02,  9.1311e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.9547e-02,  6.9334e-03,  2.4767e-02],\n",
-       "           [ 4.5120e-03,  5.6573e-05,  2.2278e-02],\n",
-       "           [-2.3444e-02, -2.0542e-02,  1.9639e-02]],\n",
-       " \n",
-       "          [[-3.7779e-03, -2.0886e-02,  1.6565e-02],\n",
-       "           [ 2.2457e-02,  1.6672e-02,  1.6637e-02],\n",
-       "           [ 7.6850e-03,  4.2127e-03,  1.3066e-02]],\n",
-       " \n",
-       "          [[-1.4837e-02,  1.1837e-02, -1.2164e-03],\n",
-       "           [-9.1435e-03,  1.3393e-02, -8.1665e-03],\n",
-       "           [ 4.6593e-03,  1.7219e-02, -2.2801e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-4.5842e-03, -3.2178e-02, -1.9973e-02],\n",
-       "           [-2.2946e-02,  1.2193e-02,  1.6906e-02],\n",
-       "           [-2.7516e-02,  1.1992e-03, -5.5079e-03]],\n",
-       " \n",
-       "          [[-3.9502e-02,  4.0997e-03, -2.2547e-03],\n",
-       "           [ 9.0719e-03, -1.9541e-02, -8.0158e-03],\n",
-       "           [-4.5223e-03,  1.4730e-03, -3.1383e-02]],\n",
-       " \n",
-       "          [[-2.8293e-02, -1.7134e-02,  5.5610e-03],\n",
-       "           [-1.8595e-02, -1.3137e-02,  1.7261e-02],\n",
-       "           [ 2.4927e-02,  2.4335e-02,  1.2912e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 8.7305e-03,  1.3683e-02, -1.4206e-02],\n",
-       "           [-3.1256e-02, -5.2077e-03,  1.0944e-02],\n",
-       "           [-3.0296e-02, -6.8632e-03,  2.4722e-04]],\n",
-       " \n",
-       "          [[-3.9425e-03,  5.1052e-03,  3.6665e-03],\n",
-       "           [ 1.6466e-02,  9.7797e-03,  2.3376e-02],\n",
-       "           [-5.8406e-03, -2.8533e-02,  6.5227e-04]],\n",
-       " \n",
-       "          [[ 1.7415e-02,  8.5092e-03, -3.4711e-04],\n",
-       "           [ 3.6561e-02,  3.7250e-02,  2.6414e-02],\n",
-       "           [-6.2236e-04,  2.4310e-02,  1.8045e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.9972, 1.0102, 0.9992, 1.0049, 1.0069, 1.0148, 1.0055, 0.9909, 1.0072,\n",
-       "         1.0010, 0.9954, 1.0062, 0.9942, 1.0073, 1.0075, 0.9986, 0.9928, 1.0004,\n",
-       "         1.0141, 1.0085, 0.9951, 0.9990, 0.9947, 1.0109, 1.0123, 0.9927, 1.0050,\n",
-       "         1.0048, 0.9917, 0.9975, 1.0017, 0.9956, 0.9909, 0.9956, 0.9981, 1.0106,\n",
-       "         0.9936, 1.0020, 1.0286, 0.9995, 0.9953, 0.9971, 0.9994, 0.9938, 0.9955,\n",
-       "         0.9858, 0.9988, 0.9912, 0.9942, 1.0031, 1.0042, 0.9951, 0.9908, 1.0103,\n",
-       "         1.0081, 1.0097, 1.0031, 0.9920, 0.9932, 1.0026, 0.9891, 1.0048, 0.9969,\n",
-       "         1.0065, 1.0064, 0.9799, 1.0007, 0.9890, 1.0028, 0.9923, 0.9926, 1.0041,\n",
-       "         0.9898, 0.9967, 0.9995, 0.9995, 1.0063, 0.9985, 0.9924, 1.0102, 1.0040,\n",
-       "         0.9990, 1.0020, 0.9952, 1.0023, 0.9968, 0.9910, 1.0049, 0.9941, 0.9987,\n",
-       "         1.0134, 0.9958, 1.0095, 0.9948, 1.0011, 0.9910, 1.0010, 1.0040, 1.0051,\n",
-       "         1.0109, 1.0096, 1.0163, 1.0027, 0.9911, 1.0154, 0.9992, 0.9832, 1.0106,\n",
-       "         0.9807, 0.9999, 0.9945, 1.0015, 0.9907, 1.0063, 1.0078, 1.0007, 1.0047,\n",
-       "         1.0074, 0.9960, 0.9983, 0.9879, 1.0062, 1.0046, 0.9872, 0.9911, 1.0118,\n",
-       "         0.9941, 1.0060, 1.0072, 0.9933, 0.9982, 0.9933, 1.0008, 0.9980, 1.0037,\n",
-       "         1.0025, 0.9976, 0.9915, 0.9931, 1.0061, 0.9965, 0.9943, 1.0098, 1.0055,\n",
-       "         0.9910, 1.0100, 1.0033, 0.9958, 0.9861, 0.9973, 0.9966, 1.0015, 1.0090,\n",
-       "         1.0021, 1.0037, 1.0022, 1.0044, 1.0096, 1.0120, 0.9983, 0.9974, 1.0128,\n",
-       "         0.9843, 0.9851, 0.9979, 0.9884, 0.9987, 0.9946, 0.9948, 1.0113, 1.0025,\n",
-       "         1.0032, 1.0096, 0.9965, 1.0072, 0.9939, 0.9954, 1.0062, 1.0145, 1.0007,\n",
-       "         1.0049, 0.9898, 0.9820, 0.9857, 0.9931, 1.0093, 1.0012, 0.9937, 1.0067,\n",
-       "         0.9964, 1.0081, 1.0081, 1.0027, 0.9944, 0.9938, 1.0068, 0.9904, 0.9985,\n",
-       "         1.0206, 1.0008, 1.0042, 1.0060, 0.9902, 0.9933, 0.9953, 0.9954, 0.9967,\n",
-       "         0.9948, 0.9972, 1.0055, 1.0019, 0.9877, 1.0017, 0.9923, 1.0114, 0.9959,\n",
-       "         0.9989, 0.9930, 1.0061, 0.9941, 1.0109, 0.9915, 0.9979, 0.9825, 1.0001,\n",
-       "         1.0005, 0.9791, 0.9950, 0.9776, 1.0031, 1.0037, 1.0000, 0.9913, 0.9957,\n",
-       "         1.0122, 1.0122, 1.0009, 1.0023, 1.0018, 1.0038, 1.0035, 1.0076, 0.9964,\n",
-       "         1.0020, 0.9944, 1.0109, 0.9951, 1.0114, 0.9830, 0.9961, 0.9995, 0.9835,\n",
-       "         0.9966, 0.9999, 0.9977, 1.0023], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-8.8399e-03,  1.0936e-02, -6.0229e-03,  6.3268e-03,  2.2699e-02,\n",
-       "          3.4744e-03,  1.2129e-02, -1.0358e-02,  6.6729e-03,  1.1617e-03,\n",
-       "          1.3351e-02, -7.5476e-03,  1.3866e-02,  3.8800e-03,  6.1061e-03,\n",
-       "          1.0896e-03, -8.5561e-04,  2.4370e-02,  4.1026e-03, -1.5568e-03,\n",
-       "         -1.1257e-02,  1.9588e-02, -1.1550e-02,  5.5066e-03, -1.5398e-02,\n",
-       "         -1.7283e-02, -3.3591e-03, -1.1988e-03,  4.0307e-03,  1.1284e-02,\n",
-       "         -8.9158e-03, -2.4106e-03, -1.3734e-02,  1.5023e-02,  2.1651e-02,\n",
-       "          4.9539e-03, -4.8335e-03, -1.9716e-02, -2.4241e-03, -5.8673e-03,\n",
-       "         -4.9406e-03,  1.8124e-03, -2.8035e-02,  1.5847e-02, -1.2398e-02,\n",
-       "         -1.4500e-02, -1.9277e-03, -1.1835e-02,  5.8092e-03, -1.1975e-02,\n",
-       "          6.1201e-03,  1.2034e-03,  6.2665e-03, -3.3262e-03, -1.8585e-03,\n",
-       "         -1.3495e-02,  1.5127e-02,  1.2399e-02, -1.0284e-02, -2.3471e-03,\n",
-       "         -2.2510e-03,  3.8715e-02, -8.9429e-03, -1.7518e-03,  2.1182e-02,\n",
-       "         -1.4652e-02,  8.9913e-03, -1.3684e-02, -6.8274e-03, -4.3788e-03,\n",
-       "         -9.3449e-03,  3.2642e-02, -7.6885e-03,  7.4714e-05, -1.0475e-02,\n",
-       "          2.6731e-04,  1.3560e-02,  1.7533e-02, -1.5191e-02,  1.7379e-03,\n",
-       "         -7.2823e-03,  1.4708e-03, -1.3822e-02, -1.7434e-02, -8.3111e-03,\n",
-       "         -1.4687e-02, -1.2755e-02,  1.9110e-04, -4.5584e-03,  2.7339e-03,\n",
-       "          2.2303e-02,  9.2589e-03,  1.0447e-02, -1.9909e-03,  1.0881e-02,\n",
-       "         -1.6101e-02, -2.5204e-03, -8.8667e-03,  9.6191e-03,  2.0612e-02,\n",
-       "         -5.3397e-03,  3.6417e-03, -3.0690e-03, -2.4652e-02,  1.1331e-02,\n",
-       "         -1.5497e-02, -7.5872e-03,  1.4586e-02, -2.1267e-02,  4.3729e-03,\n",
-       "          2.8243e-03, -7.0012e-03, -1.3470e-02,  2.1791e-02,  1.7890e-02,\n",
-       "         -1.4769e-02, -8.9042e-03, -2.8131e-04,  1.9881e-02, -1.5270e-02,\n",
-       "         -8.3745e-03,  1.0742e-02,  1.3408e-02, -1.0227e-02, -9.5614e-03,\n",
-       "          1.9273e-02,  1.0705e-02, -4.5468e-03,  2.6017e-03,  1.5159e-02,\n",
-       "         -1.4432e-02, -6.2432e-03,  2.0892e-02,  1.4964e-02, -1.1521e-02,\n",
-       "         -3.6414e-03, -1.2078e-02, -9.7114e-03,  9.2441e-04,  3.9093e-03,\n",
-       "         -1.6043e-02, -8.2021e-03,  6.3183e-03, -8.0271e-03, -8.4562e-03,\n",
-       "          1.9828e-03,  1.6346e-03, -5.7102e-03,  2.2719e-03, -9.9798e-03,\n",
-       "          9.2399e-03,  6.8626e-03,  7.4649e-03,  8.0210e-03, -1.7882e-03,\n",
-       "          7.2582e-03, -5.4597e-03, -1.1156e-02,  6.3824e-03, -4.7118e-03,\n",
-       "         -6.8716e-03, -6.0900e-03, -1.2162e-02, -1.0386e-02,  8.0419e-03,\n",
-       "         -1.4370e-02,  9.7620e-03, -9.7883e-03, -1.4318e-02, -1.4401e-03,\n",
-       "         -3.5801e-04,  2.9504e-02,  2.5202e-03, -2.2063e-03, -1.1342e-02,\n",
-       "          1.8734e-02, -9.1238e-03,  5.9063e-03, -6.9279e-03,  1.4347e-03,\n",
-       "         -1.1247e-02, -1.1779e-02, -2.1900e-02, -1.6565e-02,  4.1400e-03,\n",
-       "          3.0754e-02,  3.8026e-03, -1.1308e-02,  1.7315e-02,  3.9347e-03,\n",
-       "          2.4392e-02, -5.2319e-03,  7.7892e-03,  1.8153e-03, -1.3770e-03,\n",
-       "         -5.8700e-03, -8.4903e-03,  1.5251e-02,  3.1928e-02,  5.5436e-03,\n",
-       "          2.1729e-02, -5.1955e-03, -1.4676e-02,  5.0006e-03, -6.2343e-03,\n",
-       "          1.5819e-03, -1.0398e-02, -8.6930e-03, -3.1270e-03,  5.8424e-03,\n",
-       "         -1.3238e-02, -8.6257e-03, -9.8394e-04, -8.9828e-04,  1.4825e-02,\n",
-       "          4.2872e-02,  8.5455e-03, -6.3034e-03, -1.0114e-02, -5.8385e-03,\n",
-       "         -1.0877e-02, -7.7030e-03,  1.4582e-02,  4.5145e-03,  3.8678e-03,\n",
-       "          7.1213e-03, -8.4861e-03,  2.1978e-02, -2.1232e-02, -4.7203e-04,\n",
-       "         -8.5357e-03,  9.2356e-03, -1.2539e-02, -2.1371e-02, -5.4984e-03,\n",
-       "          7.3842e-03,  8.8693e-03,  7.1167e-03,  7.2342e-03, -3.2243e-03,\n",
-       "         -1.5310e-02,  3.1198e-02, -1.2436e-02,  5.4705e-03, -2.0162e-03,\n",
-       "          1.8273e-02, -1.0978e-02, -2.5789e-04, -1.6299e-02, -2.3246e-03,\n",
-       "          1.6784e-02, -1.2100e-02, -8.9432e-03,  4.3044e-04,  3.3090e-02,\n",
-       "          2.7391e-03], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-0.0386,  0.0424,  0.0154],\n",
-       "           [-0.0280,  0.0019,  0.0197],\n",
-       "           [ 0.0097,  0.0124,  0.0134]],\n",
-       " \n",
-       "          [[-0.0307, -0.0019, -0.0370],\n",
-       "           [ 0.0566, -0.0283, -0.0232],\n",
-       "           [-0.0018,  0.0251,  0.0026]],\n",
-       " \n",
-       "          [[ 0.0044, -0.0228, -0.0384],\n",
-       "           [-0.0007, -0.0456, -0.0241],\n",
-       "           [ 0.0312, -0.0179, -0.0329]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0424, -0.0254, -0.0118],\n",
-       "           [-0.0081, -0.0366,  0.0423],\n",
-       "           [-0.0027, -0.0060,  0.0307]],\n",
-       " \n",
-       "          [[-0.0007, -0.0443, -0.0314],\n",
-       "           [-0.0075, -0.0542, -0.0506],\n",
-       "           [-0.0232,  0.0032, -0.0343]],\n",
-       " \n",
-       "          [[ 0.0074,  0.0315,  0.0178],\n",
-       "           [ 0.0090, -0.0053, -0.0507],\n",
-       "           [ 0.0414,  0.0253, -0.0240]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0406,  0.0324, -0.0234],\n",
-       "           [ 0.0322,  0.0311,  0.0152],\n",
-       "           [-0.0091, -0.0390, -0.0114]],\n",
-       " \n",
-       "          [[-0.0270, -0.0016, -0.0505],\n",
-       "           [-0.0383, -0.0128, -0.0045],\n",
-       "           [ 0.0226, -0.0337,  0.0075]],\n",
-       " \n",
-       "          [[ 0.0355, -0.0066,  0.0198],\n",
-       "           [-0.0519, -0.0461,  0.0281],\n",
-       "           [ 0.0391, -0.0049,  0.0299]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0003,  0.0295, -0.0477],\n",
-       "           [ 0.0341, -0.0367,  0.0072],\n",
-       "           [ 0.0186, -0.0323, -0.0536]],\n",
-       " \n",
-       "          [[-0.0063, -0.0258,  0.0097],\n",
-       "           [-0.0399, -0.0120, -0.0009],\n",
-       "           [-0.0523,  0.0288,  0.0143]],\n",
-       " \n",
-       "          [[ 0.0364, -0.0452,  0.0129],\n",
-       "           [-0.0161,  0.0475,  0.0117],\n",
-       "           [ 0.0183,  0.0466,  0.0208]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0037,  0.0504,  0.0087],\n",
-       "           [-0.0395,  0.0127,  0.0478],\n",
-       "           [ 0.0281, -0.0238,  0.0305]],\n",
-       " \n",
-       "          [[ 0.0303, -0.0455, -0.0423],\n",
-       "           [-0.0258, -0.0507, -0.0504],\n",
-       "           [-0.0190, -0.0073, -0.0358]],\n",
-       " \n",
-       "          [[ 0.0519, -0.0134,  0.0065],\n",
-       "           [ 0.0278, -0.0200,  0.0538],\n",
-       "           [ 0.0499, -0.0329,  0.0273]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0220, -0.0192,  0.0110],\n",
-       "           [-0.0374, -0.0436, -0.0072],\n",
-       "           [ 0.0086, -0.0337,  0.0314]],\n",
-       " \n",
-       "          [[-0.0328, -0.0190,  0.0344],\n",
-       "           [ 0.0462, -0.0295, -0.0454],\n",
-       "           [ 0.0212,  0.0160, -0.0362]],\n",
-       " \n",
-       "          [[-0.0112, -0.0140,  0.0281],\n",
-       "           [ 0.0274,  0.0396, -0.0009],\n",
-       "           [ 0.0522,  0.0316,  0.0023]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0352, -0.0010,  0.0031],\n",
-       "           [-0.0386,  0.0006, -0.0152],\n",
-       "           [ 0.0053, -0.0326, -0.0117]],\n",
-       " \n",
-       "          [[ 0.0264,  0.0331,  0.0064],\n",
-       "           [ 0.0427, -0.0182, -0.0171],\n",
-       "           [-0.0128, -0.0126,  0.0438]],\n",
-       " \n",
-       "          [[ 0.0456,  0.0206,  0.0304],\n",
-       "           [ 0.0259, -0.0211,  0.0607],\n",
-       "           [ 0.0320,  0.0314,  0.0353]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0363, -0.0119,  0.0418],\n",
-       "           [-0.0183, -0.0285, -0.0230],\n",
-       "           [ 0.0471, -0.0478, -0.0307]],\n",
-       " \n",
-       "          [[ 0.0396, -0.0426, -0.0183],\n",
-       "           [-0.0260, -0.0374,  0.0037],\n",
-       "           [ 0.0349, -0.0305,  0.0319]],\n",
-       " \n",
-       "          [[-0.0175,  0.0452, -0.0050],\n",
-       "           [-0.0179,  0.0433, -0.0087],\n",
-       "           [ 0.0328, -0.0027,  0.0061]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0203,  0.0281, -0.0207],\n",
-       "           [-0.0302,  0.0271, -0.0420],\n",
-       "           [-0.0185, -0.0265,  0.0195]],\n",
-       " \n",
-       "          [[ 0.0443, -0.0248, -0.0047],\n",
-       "           [ 0.0311, -0.0179,  0.0322],\n",
-       "           [-0.0076, -0.0254, -0.0348]],\n",
-       " \n",
-       "          [[-0.0221, -0.0093, -0.0118],\n",
-       "           [ 0.0362, -0.0398, -0.0070],\n",
-       "           [-0.0341,  0.0294, -0.0156]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0239, -0.0103, -0.0084],\n",
-       "           [ 0.0404, -0.0177,  0.0466],\n",
-       "           [ 0.0042,  0.0273, -0.0073]],\n",
-       " \n",
-       "          [[ 0.0155, -0.0118,  0.0498],\n",
-       "           [ 0.0161,  0.0606, -0.0324],\n",
-       "           [ 0.0418,  0.0066,  0.0073]],\n",
-       " \n",
-       "          [[ 0.0105,  0.0420,  0.0061],\n",
-       "           [ 0.0005,  0.0300, -0.0076],\n",
-       "           [ 0.0350, -0.0365, -0.0124]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0078,  0.0079, -0.0364],\n",
-       "           [-0.0405, -0.0067,  0.0412],\n",
-       "           [ 0.0535,  0.0446, -0.0090]],\n",
-       " \n",
-       "          [[-0.0270, -0.0165, -0.0498],\n",
-       "           [ 0.0137,  0.0145, -0.0323],\n",
-       "           [ 0.0080, -0.0316,  0.0009]],\n",
-       " \n",
-       "          [[-0.0529, -0.0237,  0.0494],\n",
-       "           [-0.0184, -0.0249,  0.0380],\n",
-       "           [ 0.0366, -0.0224, -0.0128]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0397, -0.0395, -0.0105],\n",
-       "           [ 0.0387,  0.0124, -0.0312],\n",
-       "           [ 0.0010,  0.0351, -0.0390]],\n",
-       " \n",
-       "          [[-0.0130, -0.0096, -0.0025],\n",
-       "           [ 0.0328,  0.0154,  0.0504],\n",
-       "           [ 0.0292, -0.0160,  0.0496]],\n",
-       " \n",
-       "          [[-0.0388,  0.0150,  0.0187],\n",
-       "           [ 0.0363,  0.0344, -0.0157],\n",
-       "           [ 0.0084,  0.0312,  0.0044]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.9905, 0.9924, 0.9980, 0.9858, 0.9845, 1.0053, 0.9795, 0.9904, 0.9895,\n",
-       "         0.9851, 0.9697, 0.9983, 0.9788, 0.9981, 1.0026, 0.9899, 1.0017, 1.0003,\n",
-       "         0.9962, 0.9847, 0.9904, 0.9964, 1.0002, 0.9898, 0.9916, 1.0026, 0.9829,\n",
-       "         0.9789, 0.9720, 1.0173, 0.9916, 0.9799, 0.9901, 0.9924, 0.9880, 0.9912,\n",
-       "         0.9820, 0.9873, 0.9927, 0.9871, 0.9842, 0.9911, 0.9844, 0.9840, 0.9924,\n",
-       "         1.0011, 0.9987, 0.9872, 0.9847, 0.9958, 0.9894, 0.9928, 0.9792, 0.9769,\n",
-       "         1.0045, 0.9952, 0.9899, 0.9977, 0.9714, 0.9843, 0.9858, 0.9835, 0.9935,\n",
-       "         0.9882, 0.9839, 0.9796, 0.9720, 0.9633, 0.9799, 0.9885, 0.9878, 0.9972,\n",
-       "         0.9746, 0.9727, 0.9911, 0.9897, 0.9871, 0.9944, 1.0204, 0.9917, 0.9898,\n",
-       "         0.9942, 0.9809, 1.0044, 0.9990, 0.9838, 0.9680, 0.9962, 0.9839, 0.9908,\n",
-       "         1.0019, 0.9912, 1.0105, 0.9925, 0.9932, 1.0058, 0.9747, 0.9857, 0.9962,\n",
-       "         0.9933, 0.9924, 0.9958, 0.9909, 0.9993, 0.9903, 0.9796, 0.9975, 0.9913,\n",
-       "         0.9952, 0.9891, 0.9937, 0.9823, 1.0060, 0.9783, 0.9800, 0.9967, 0.9898,\n",
-       "         0.9814, 1.0009, 0.9900, 0.9967, 0.9963, 0.9948, 0.9972, 0.9986, 0.9974,\n",
-       "         0.9921, 0.9994, 0.9906, 0.9785, 0.9825, 0.9884, 0.9826, 0.9857, 0.9861,\n",
-       "         1.0056, 1.0017, 0.9628, 0.9910, 0.9886, 0.9923, 0.9896, 0.9805, 0.9948,\n",
-       "         0.9942, 0.9906, 0.9894, 0.9724, 1.0052, 0.9911, 1.0009, 0.9948, 0.9939,\n",
-       "         0.9925, 0.9825, 0.9699, 0.9738, 0.9977, 0.9909, 0.9867, 0.9996, 1.0087,\n",
-       "         0.9982, 1.0086, 1.0029, 0.9917, 0.9552, 0.9993, 0.9739, 0.9833, 0.9987,\n",
-       "         0.9879, 1.0331, 0.9881, 0.9998, 0.9984, 1.0111, 0.9815, 0.9896, 0.9915,\n",
-       "         0.9976, 0.9750, 0.9988, 1.0016, 1.0015, 0.9880, 0.9955, 1.0068, 0.9842,\n",
-       "         0.9837, 0.9976, 0.9885, 0.9747, 0.9858, 0.9680, 0.9776, 0.9899, 0.9888,\n",
-       "         0.9868, 0.9812, 0.9958, 1.0258, 0.9744, 0.9815, 0.9934, 1.0425, 0.9803,\n",
-       "         0.9808, 1.0119, 0.9808, 1.0032, 0.9877, 0.9970, 1.0006, 0.9867, 0.9799,\n",
-       "         0.9846, 0.9914, 0.9751, 0.9853, 0.9963, 0.9873, 1.0177, 0.9975, 0.9734,\n",
-       "         0.9851, 0.9932, 1.0045, 0.9948, 0.9975, 0.9935, 1.0036, 0.9845, 0.9932,\n",
-       "         0.9822, 0.9997, 0.9844, 0.9977, 0.9943, 0.9873, 0.9853, 1.0010, 1.0035,\n",
-       "         1.0052, 0.9937, 0.9827, 1.0058, 0.9819, 1.0135, 0.9955, 0.9989, 0.9856,\n",
-       "         0.9839, 0.9630, 0.9951, 0.9864], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-9.4982e-03, -1.2589e-03, -1.4899e-02, -2.0090e-02, -4.4536e-03,\n",
-       "          3.0329e-03, -5.0093e-03, -1.6785e-02,  7.7337e-03, -3.3730e-03,\n",
-       "         -3.2104e-02, -2.6240e-02, -2.6508e-02,  6.1430e-03,  1.3792e-03,\n",
-       "         -1.1048e-02, -7.0179e-04, -1.4676e-02, -1.0496e-02, -9.3349e-03,\n",
-       "         -2.2309e-02,  2.2506e-02,  1.2002e-03, -1.2974e-02, -1.0590e-02,\n",
-       "         -5.1966e-03, -8.8312e-03, -4.3246e-03, -1.6162e-02,  5.2603e-03,\n",
-       "         -1.0440e-02, -3.1561e-03,  8.8222e-04, -7.3634e-03, -1.4242e-02,\n",
-       "         -1.6768e-02, -2.8400e-02, -5.3530e-03, -3.4942e-03, -2.3774e-02,\n",
-       "         -8.5136e-03, -9.9849e-03, -3.0841e-03, -1.5082e-02, -2.0910e-02,\n",
-       "         -1.0924e-04, -2.8663e-03, -7.2082e-03, -1.2999e-02, -1.1217e-02,\n",
-       "         -1.2899e-02, -1.5204e-02, -2.8291e-02, -4.5842e-05, -1.1649e-02,\n",
-       "         -4.0572e-03, -1.3523e-02,  3.7733e-03, -1.0728e-02, -3.1263e-03,\n",
-       "         -5.5726e-03, -1.1952e-02, -1.5578e-02,  5.2480e-03, -3.3846e-03,\n",
-       "         -9.3493e-03, -2.8698e-03, -2.4517e-02, -1.3910e-02, -3.0074e-02,\n",
-       "         -9.9540e-03, -2.6719e-02, -2.0523e-02, -8.7122e-03, -7.1926e-03,\n",
-       "         -5.1822e-03, -6.3796e-03, -3.0216e-02,  1.9771e-02, -1.0188e-02,\n",
-       "         -1.2615e-02, -1.6334e-02, -2.4313e-02,  8.6739e-03,  6.8655e-04,\n",
-       "         -9.1557e-03, -2.5605e-02, -1.4608e-02, -1.8851e-02, -6.0863e-03,\n",
-       "          1.0389e-02,  1.2002e-02,  1.4998e-02, -7.6047e-03, -1.3700e-02,\n",
-       "          4.9412e-03, -2.0343e-02, -4.6158e-03, -4.3070e-03,  2.3952e-03,\n",
-       "         -1.3969e-02, -3.8218e-03, -1.4180e-02, -1.2852e-05, -2.6368e-02,\n",
-       "         -1.7067e-02, -1.5393e-02, -1.8128e-02, -4.5673e-03, -1.7644e-02,\n",
-       "         -1.1281e-02,  3.5596e-04, -1.0713e-02, -1.4895e-02, -2.8723e-03,\n",
-       "          8.0200e-07, -1.0294e-02, -9.0466e-03,  2.0620e-03, -1.0601e-02,\n",
-       "          2.2020e-02, -2.6054e-03, -1.7138e-02,  1.3390e-03, -1.4541e-02,\n",
-       "          4.3375e-04, -1.7076e-02,  6.5591e-03, -7.8862e-03, -8.8234e-03,\n",
-       "         -8.7396e-03, -5.1504e-03, -1.1441e-02,  1.5802e-03, -1.4086e-02,\n",
-       "         -7.7128e-03,  2.9910e-03, -2.9740e-02, -8.7661e-03, -2.4874e-02,\n",
-       "          1.4874e-03, -7.3963e-03, -9.9868e-03, -9.7424e-03, -7.8608e-04,\n",
-       "          2.2015e-02, -7.4351e-03,  5.4329e-04, -7.6062e-03, -8.1088e-03,\n",
-       "          1.5541e-02, -1.4536e-02, -3.4744e-04, -3.5763e-03, -1.6041e-02,\n",
-       "         -9.4030e-05, -1.0792e-02,  1.2159e-02,  3.8543e-03, -1.4166e-02,\n",
-       "          7.7452e-03,  1.4173e-02, -2.7417e-03,  1.5665e-02,  2.0281e-03,\n",
-       "         -1.5999e-02, -2.2748e-02, -2.1083e-02, -3.0329e-02, -1.0979e-02,\n",
-       "         -4.3237e-05, -1.2801e-03,  1.9977e-02, -1.2929e-02,  1.7388e-02,\n",
-       "         -5.2887e-03,  1.1216e-02, -9.3505e-03, -3.6503e-02, -2.2196e-02,\n",
-       "         -2.1978e-03, -6.3802e-03, -6.8834e-03, -9.5707e-03, -3.2210e-03,\n",
-       "         -1.9688e-02,  3.3132e-03,  2.0489e-02, -1.8962e-02, -4.9886e-03,\n",
-       "          8.5227e-03, -8.6968e-03, -1.7249e-02, -2.5475e-03, -2.8296e-02,\n",
-       "         -6.9524e-03, -1.3638e-02, -1.3089e-02, -1.0470e-02, -1.9991e-02,\n",
-       "         -1.3677e-02,  3.3903e-02, -1.6243e-02, -1.6153e-02, -3.9355e-03,\n",
-       "          3.0426e-02, -1.8647e-02, -1.5623e-02,  1.1556e-02, -1.0422e-02,\n",
-       "          1.6108e-02, -2.2533e-02, -9.0304e-03, -9.0069e-04, -1.5852e-02,\n",
-       "         -4.1344e-03, -2.3483e-02,  6.7284e-03, -1.7568e-02,  1.6291e-03,\n",
-       "          5.2886e-03, -1.0618e-02, -1.4753e-02, -1.1984e-02, -2.6752e-03,\n",
-       "         -1.2535e-02,  4.2503e-04,  2.1562e-03, -1.4963e-02, -5.0249e-03,\n",
-       "         -8.8051e-04,  6.5362e-04, -6.5142e-03, -1.3503e-02, -1.2741e-02,\n",
-       "         -2.3820e-02, -1.2628e-02,  1.3321e-03,  1.9135e-04, -1.5159e-02,\n",
-       "         -9.4040e-03,  5.4824e-03,  8.1268e-03, -9.6743e-03, -5.8381e-03,\n",
-       "         -1.5884e-02, -6.4852e-04, -1.6525e-02,  1.7210e-02, -8.7558e-03,\n",
-       "         -6.7120e-03, -1.7322e-02, -6.1796e-03, -2.2355e-02,  1.7834e-03,\n",
-       "         -2.2590e-02], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 0.0698,  0.0618,  0.0002],\n",
-       "           [ 0.0059, -0.0124, -0.0057],\n",
-       "           [ 0.0203, -0.0092,  0.0052]],\n",
-       " \n",
-       "          [[-0.0022,  0.0135, -0.0391],\n",
-       "           [ 0.0037,  0.0084, -0.0025],\n",
-       "           [-0.0174,  0.0257,  0.0397]],\n",
-       " \n",
-       "          [[ 0.0096, -0.0068,  0.0012],\n",
-       "           [ 0.0168, -0.0102,  0.0219],\n",
-       "           [-0.0114, -0.0516, -0.0115]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0066,  0.0103,  0.0035],\n",
-       "           [ 0.0158,  0.0208,  0.0191],\n",
-       "           [-0.0014, -0.0434, -0.0114]],\n",
-       " \n",
-       "          [[ 0.0298, -0.0361, -0.0406],\n",
-       "           [-0.0279, -0.0120,  0.0167],\n",
-       "           [-0.0011, -0.0322, -0.0127]],\n",
-       " \n",
-       "          [[-0.0304,  0.0267,  0.0134],\n",
-       "           [-0.0501, -0.0473, -0.0427],\n",
-       "           [-0.0087,  0.0320, -0.0373]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0298,  0.0310,  0.0097],\n",
-       "           [ 0.0398,  0.0253, -0.0110],\n",
-       "           [-0.0166, -0.0190,  0.0057]],\n",
-       " \n",
-       "          [[ 0.0181,  0.0190,  0.0245],\n",
-       "           [-0.0257,  0.0339, -0.0117],\n",
-       "           [-0.0146, -0.0452, -0.0193]],\n",
-       " \n",
-       "          [[-0.0122, -0.0125, -0.0264],\n",
-       "           [ 0.0031, -0.0186, -0.0272],\n",
-       "           [-0.0113,  0.0045,  0.0064]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0134,  0.0232,  0.0121],\n",
-       "           [-0.0051, -0.0091,  0.0158],\n",
-       "           [ 0.0179,  0.0228,  0.0121]],\n",
-       " \n",
-       "          [[ 0.0111, -0.0004,  0.0370],\n",
-       "           [-0.0046,  0.0049,  0.0286],\n",
-       "           [ 0.0443, -0.0188,  0.0115]],\n",
-       " \n",
-       "          [[ 0.0147, -0.0006, -0.0079],\n",
-       "           [ 0.0270,  0.0524, -0.0234],\n",
-       "           [ 0.0025,  0.0488, -0.0180]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0103, -0.0347, -0.0293],\n",
-       "           [-0.0151, -0.0021, -0.0079],\n",
-       "           [-0.0470,  0.0323, -0.0117]],\n",
-       " \n",
-       "          [[-0.0128,  0.0252, -0.0039],\n",
-       "           [-0.0277, -0.0435, -0.0477],\n",
-       "           [-0.0155, -0.0404, -0.0337]],\n",
-       " \n",
-       "          [[-0.0130, -0.0332, -0.0511],\n",
-       "           [-0.0034, -0.0139,  0.0220],\n",
-       "           [ 0.0066,  0.0035, -0.0154]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0498,  0.0118,  0.0326],\n",
-       "           [ 0.0356,  0.0376, -0.0001],\n",
-       "           [-0.0278, -0.0078,  0.0294]],\n",
-       " \n",
-       "          [[ 0.0296, -0.0134,  0.0055],\n",
-       "           [ 0.0201,  0.0240, -0.0250],\n",
-       "           [ 0.0040, -0.0124, -0.0404]],\n",
-       " \n",
-       "          [[ 0.0463,  0.0494,  0.0254],\n",
-       "           [ 0.0589,  0.0649,  0.0104],\n",
-       "           [-0.0035,  0.0350,  0.0308]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-0.0075,  0.0018, -0.0018],\n",
-       "           [ 0.0270, -0.0064, -0.0369],\n",
-       "           [ 0.0047, -0.0216,  0.0181]],\n",
-       " \n",
-       "          [[ 0.0164,  0.0016,  0.0425],\n",
-       "           [ 0.0393,  0.0027, -0.0191],\n",
-       "           [ 0.0375,  0.0083,  0.0072]],\n",
-       " \n",
-       "          [[-0.0209, -0.0245, -0.0257],\n",
-       "           [-0.0162,  0.0032,  0.0154],\n",
-       "           [-0.0064, -0.0018,  0.0015]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0143, -0.0281,  0.0133],\n",
-       "           [ 0.0061,  0.0285,  0.0060],\n",
-       "           [-0.0109,  0.0261,  0.0034]],\n",
-       " \n",
-       "          [[ 0.0031,  0.0035, -0.0224],\n",
-       "           [-0.0026,  0.0235,  0.0425],\n",
-       "           [ 0.0094,  0.0406, -0.0146]],\n",
-       " \n",
-       "          [[-0.0487, -0.0534, -0.0051],\n",
-       "           [-0.0202,  0.0225, -0.0330],\n",
-       "           [-0.0341, -0.0070, -0.0170]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0160,  0.0228,  0.0380],\n",
-       "           [ 0.0359, -0.0342,  0.0300],\n",
-       "           [ 0.0003,  0.0128, -0.0091]],\n",
-       " \n",
-       "          [[ 0.0105, -0.0463, -0.0450],\n",
-       "           [-0.0157, -0.0273,  0.0286],\n",
-       "           [ 0.0205,  0.0196,  0.0333]],\n",
-       " \n",
-       "          [[-0.0079, -0.0260,  0.0069],\n",
-       "           [-0.0254, -0.0396,  0.0168],\n",
-       "           [ 0.0027, -0.0102, -0.0376]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0350, -0.0082,  0.0039],\n",
-       "           [ 0.0264,  0.0083,  0.0065],\n",
-       "           [-0.0086,  0.0334, -0.0263]],\n",
-       " \n",
-       "          [[ 0.0101,  0.0124,  0.0498],\n",
-       "           [ 0.0477,  0.0672,  0.0503],\n",
-       "           [ 0.0937,  0.0138,  0.0111]],\n",
-       " \n",
-       "          [[-0.0208, -0.0321, -0.0206],\n",
-       "           [-0.0408,  0.0252,  0.0129],\n",
-       "           [-0.0442, -0.0278,  0.0075]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0192,  0.0083, -0.0200],\n",
-       "           [-0.0227, -0.0113, -0.0197],\n",
-       "           [ 0.0385,  0.0181,  0.0261]],\n",
-       " \n",
-       "          [[-0.0223,  0.0182,  0.0050],\n",
-       "           [ 0.0056,  0.0021,  0.0014],\n",
-       "           [ 0.0143, -0.0356,  0.0215]],\n",
-       " \n",
-       "          [[ 0.0072, -0.0284, -0.0369],\n",
-       "           [ 0.0038, -0.0370, -0.0306],\n",
-       "           [ 0.0123, -0.0443, -0.0041]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0146,  0.0183, -0.0080],\n",
-       "           [-0.0059, -0.0198,  0.0225],\n",
-       "           [ 0.0356,  0.0295, -0.0235]],\n",
-       " \n",
-       "          [[-0.0202, -0.0273,  0.0177],\n",
-       "           [ 0.0102, -0.0163, -0.0242],\n",
-       "           [ 0.0030,  0.0241,  0.0498]],\n",
-       " \n",
-       "          [[ 0.0294,  0.0082,  0.0479],\n",
-       "           [ 0.0287,  0.0365,  0.0303],\n",
-       "           [ 0.0208,  0.0380,  0.0391]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([1.0035, 0.9809, 1.0055, 0.9951, 1.0172, 0.9865, 1.0265, 0.9841, 0.9828,\n",
-       "         1.0145, 0.9827, 0.9905, 1.0088, 0.9857, 1.0006, 0.9994, 1.0009, 0.9837,\n",
-       "         0.9681, 1.0035, 1.0025, 1.0181, 1.0065, 1.0128, 1.0204, 1.0001, 1.0072,\n",
-       "         0.9852, 0.9905, 1.0133, 1.0028, 1.0212, 0.9888, 1.0104, 0.9917, 0.9839,\n",
-       "         0.9800, 0.9920, 1.0121, 1.0030, 0.9804, 1.0012, 1.0322, 0.9899, 0.9974,\n",
-       "         1.0010, 0.9905, 1.0059, 0.9988, 1.0179, 1.0235, 1.0086, 1.0344, 1.0057,\n",
-       "         0.9901, 1.0008, 0.9996, 0.9953, 0.9985, 1.0067, 0.9893, 0.9856, 0.9815,\n",
-       "         1.0025, 1.0125, 0.9934, 1.0192, 1.0039, 0.9871, 0.9865, 1.0009, 1.0063,\n",
-       "         1.0062, 1.0147, 0.9659, 1.0123, 0.9744, 0.9962, 0.9869, 0.9821, 0.9779,\n",
-       "         1.0030, 1.0083, 0.9898, 1.0302, 1.0169, 0.9823, 1.0211, 1.0041, 1.0118,\n",
-       "         0.9960, 0.9838, 1.0232, 0.9984, 1.0060, 1.0048, 0.9813, 1.0097, 0.9915,\n",
-       "         1.0170, 0.9983, 0.9863, 1.0201, 0.9949, 1.0147, 0.9842, 0.9666, 0.9683,\n",
-       "         1.0011, 0.9905, 0.9994, 0.9876, 0.9856, 1.0023, 0.9941, 1.0042, 0.9912,\n",
-       "         1.0288, 0.9861, 1.0248, 1.0002, 0.9982, 0.9719, 0.9906, 0.9836, 0.9917,\n",
-       "         0.9971, 0.9893], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 1.1881e-02, -1.3796e-02, -1.9806e-02,  6.9497e-04,  1.6214e-02,\n",
-       "         -3.2174e-02,  2.4003e-02, -1.4696e-02, -1.3097e-02, -4.2520e-03,\n",
-       "          2.0129e-03, -1.4111e-02,  8.3971e-03, -1.1068e-02,  1.3333e-02,\n",
-       "         -7.0586e-03,  2.3565e-03, -2.8420e-03, -1.3185e-02,  3.1351e-03,\n",
-       "          7.4491e-05,  2.4128e-02, -1.2377e-02,  1.4850e-02, -1.2080e-02,\n",
-       "         -3.6471e-03, -1.2995e-02,  4.4005e-03, -2.2985e-02,  1.2033e-02,\n",
-       "          1.0271e-02, -2.0489e-02, -2.2788e-02,  2.8364e-03,  1.4532e-02,\n",
-       "          3.0275e-03, -3.8071e-02, -2.3672e-02, -6.9946e-04, -1.5570e-02,\n",
-       "         -1.5308e-02, -9.1925e-03,  3.9311e-03, -2.5932e-02, -5.9416e-03,\n",
-       "          2.6850e-02,  1.6232e-02, -1.8695e-02, -2.3911e-03, -1.1190e-02,\n",
-       "         -7.6878e-03,  1.6737e-02,  2.6197e-02, -3.5644e-03, -2.6555e-02,\n",
-       "         -1.5144e-03, -6.4907e-03,  4.6198e-02,  1.6364e-02, -1.5776e-02,\n",
-       "         -6.2261e-03, -1.0672e-02, -3.5374e-02, -3.4515e-02,  8.3627e-03,\n",
-       "         -7.6859e-03,  6.8471e-03, -3.2212e-02, -1.5040e-02,  1.4148e-03,\n",
-       "          6.7317e-03, -1.5377e-02,  3.6671e-03,  1.7247e-02, -2.5538e-02,\n",
-       "         -1.2312e-02, -3.2670e-02, -1.0084e-02, -3.2554e-02, -1.3849e-02,\n",
-       "         -6.7577e-04, -1.5473e-02,  6.0447e-03, -2.1476e-02, -1.1370e-02,\n",
-       "          3.0573e-02, -1.8624e-02,  2.0749e-02,  1.3117e-03, -4.4446e-04,\n",
-       "         -1.7983e-02, -1.3710e-02,  4.8825e-02,  2.0182e-03, -2.1544e-02,\n",
-       "         -2.3080e-02,  3.0301e-02, -3.2391e-02, -4.9881e-03, -2.5777e-02,\n",
-       "          2.2820e-03, -7.8700e-03,  4.0826e-02, -9.6280e-03,  1.1528e-02,\n",
-       "          3.4078e-02, -2.9021e-02, -8.1430e-03,  1.5056e-02, -3.7263e-02,\n",
-       "          2.3301e-03, -3.4445e-03,  2.2524e-02, -1.9441e-02, -2.6636e-02,\n",
-       "          3.2270e-02, -2.6198e-02, -6.0435e-03,  1.0636e-02, -7.5999e-03,\n",
-       "          3.7455e-03, -1.5343e-02, -2.0889e-02,  2.4855e-02, -2.5189e-02,\n",
-       "         -4.5964e-02,  2.7716e-02,  1.9633e-03], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 0.0125,  0.0203, -0.0411],\n",
-       "           [-0.0006, -0.0377,  0.0699],\n",
-       "           [-0.0052,  0.0248,  0.0376]],\n",
-       " \n",
-       "          [[ 0.0108, -0.0059, -0.0385],\n",
-       "           [-0.0726,  0.0156,  0.0326],\n",
-       "           [ 0.0128,  0.0082, -0.0566]],\n",
-       " \n",
-       "          [[-0.0453, -0.0017, -0.0495],\n",
-       "           [ 0.0314,  0.0049, -0.0517],\n",
-       "           [ 0.0336,  0.0176, -0.0375]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0653,  0.0380, -0.0618],\n",
-       "           [ 0.0181,  0.0194, -0.0237],\n",
-       "           [-0.0158, -0.0006, -0.0289]],\n",
-       " \n",
-       "          [[ 0.0530,  0.0710,  0.0003],\n",
-       "           [ 0.1015,  0.0315, -0.0088],\n",
-       "           [ 0.0162,  0.0901,  0.0715]],\n",
-       " \n",
-       "          [[ 0.0779, -0.0315,  0.0458],\n",
-       "           [-0.0094,  0.0167, -0.0227],\n",
-       "           [ 0.0437,  0.0055, -0.0058]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0360, -0.0826, -0.0635],\n",
-       "           [-0.0517,  0.0011,  0.0032],\n",
-       "           [ 0.0525, -0.0285,  0.0159]],\n",
-       " \n",
-       "          [[-0.0266, -0.0626, -0.0214],\n",
-       "           [-0.0232, -0.0164, -0.0441],\n",
-       "           [ 0.0518,  0.0102,  0.0156]],\n",
-       " \n",
-       "          [[-0.0742, -0.0837,  0.0145],\n",
-       "           [-0.0481, -0.0330, -0.0516],\n",
-       "           [-0.0812, -0.0718,  0.0433]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0032, -0.0502,  0.0464],\n",
-       "           [ 0.0619,  0.0330, -0.0757],\n",
-       "           [ 0.0080, -0.0020, -0.0185]],\n",
-       " \n",
-       "          [[-0.0817,  0.0098, -0.0543],\n",
-       "           [-0.0601,  0.0086, -0.0045],\n",
-       "           [-0.0777, -0.0141,  0.0293]],\n",
-       " \n",
-       "          [[-0.0096,  0.0040,  0.0574],\n",
-       "           [ 0.0298,  0.0170, -0.0064],\n",
-       "           [-0.0239, -0.0105, -0.0280]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0689,  0.0041, -0.0297],\n",
-       "           [-0.0019, -0.0656,  0.0384],\n",
-       "           [ 0.0523, -0.0467,  0.0260]],\n",
-       " \n",
-       "          [[ 0.0746,  0.0486, -0.0340],\n",
-       "           [ 0.0328,  0.0624,  0.0660],\n",
-       "           [ 0.0738,  0.0666,  0.0169]],\n",
-       " \n",
-       "          [[ 0.0661,  0.0177, -0.0300],\n",
-       "           [ 0.0326,  0.0157,  0.0325],\n",
-       "           [ 0.0524, -0.0256, -0.0261]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0249, -0.0191,  0.0453],\n",
-       "           [ 0.0246, -0.0474,  0.0312],\n",
-       "           [-0.0325,  0.0008,  0.0227]],\n",
-       " \n",
-       "          [[ 0.0399,  0.0577, -0.0676],\n",
-       "           [-0.0522, -0.0584, -0.0211],\n",
-       "           [-0.0392, -0.0385, -0.0371]],\n",
-       " \n",
-       "          [[ 0.0606,  0.0238, -0.0385],\n",
-       "           [ 0.0168,  0.0761,  0.0624],\n",
-       "           [-0.0176,  0.0502, -0.0407]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-0.0540, -0.0186,  0.0495],\n",
-       "           [-0.0853, -0.0551, -0.0473],\n",
-       "           [ 0.0282, -0.0227, -0.0026]],\n",
-       " \n",
-       "          [[ 0.0532, -0.0468,  0.0895],\n",
-       "           [-0.0127, -0.0275,  0.0050],\n",
-       "           [ 0.0075,  0.0002, -0.0334]],\n",
-       " \n",
-       "          [[ 0.0084, -0.0201, -0.0487],\n",
-       "           [ 0.0195,  0.0526, -0.0547],\n",
-       "           [ 0.0547,  0.0126, -0.0055]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0443,  0.0294, -0.0500],\n",
-       "           [-0.0094, -0.0013, -0.0317],\n",
-       "           [-0.0335,  0.0060, -0.0587]],\n",
-       " \n",
-       "          [[-0.0249, -0.0764, -0.0400],\n",
-       "           [ 0.0666, -0.0379, -0.0730],\n",
-       "           [ 0.0141, -0.0160, -0.0678]],\n",
-       " \n",
-       "          [[-0.0463, -0.0663,  0.0104],\n",
-       "           [-0.0309,  0.0625,  0.0068],\n",
-       "           [-0.0169, -0.0286, -0.0575]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0296,  0.0298,  0.0248],\n",
-       "           [-0.0240, -0.0078,  0.0505],\n",
-       "           [ 0.0322,  0.0185,  0.0040]],\n",
-       " \n",
-       "          [[-0.0068,  0.0282, -0.0657],\n",
-       "           [ 0.0179,  0.0002,  0.0326],\n",
-       "           [ 0.0655, -0.0686, -0.0384]],\n",
-       " \n",
-       "          [[ 0.0241, -0.0460,  0.0483],\n",
-       "           [-0.0538,  0.0560,  0.0370],\n",
-       "           [ 0.0212,  0.0185, -0.0680]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0454, -0.0256,  0.0433],\n",
-       "           [-0.0318,  0.0180, -0.0798],\n",
-       "           [-0.0137, -0.0412, -0.0039]],\n",
-       " \n",
-       "          [[-0.0554, -0.0407, -0.0129],\n",
-       "           [ 0.0621,  0.0488,  0.0450],\n",
-       "           [-0.0476, -0.0207, -0.0437]],\n",
-       " \n",
-       "          [[ 0.0617, -0.0004,  0.0539],\n",
-       "           [ 0.0422,  0.0387,  0.0172],\n",
-       "           [ 0.0197,  0.0190, -0.0565]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0610, -0.0160,  0.0218],\n",
-       "           [-0.0428,  0.0399, -0.0118],\n",
-       "           [-0.0274,  0.0112,  0.0628]],\n",
-       " \n",
-       "          [[ 0.0164, -0.0358, -0.0186],\n",
-       "           [ 0.0150,  0.0473,  0.0239],\n",
-       "           [-0.0731, -0.0382, -0.0501]],\n",
-       " \n",
-       "          [[ 0.0044, -0.0060, -0.0202],\n",
-       "           [ 0.0289, -0.0251,  0.0682],\n",
-       "           [ 0.0424, -0.0118, -0.0236]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0614, -0.0737,  0.0419],\n",
-       "           [ 0.0274,  0.0374, -0.0029],\n",
-       "           [-0.0315,  0.0377, -0.0545]],\n",
-       " \n",
-       "          [[ 0.0376,  0.0006,  0.0524],\n",
-       "           [ 0.0326, -0.0234, -0.0610],\n",
-       "           [-0.0650,  0.0745, -0.0525]],\n",
-       " \n",
-       "          [[-0.0542,  0.0569, -0.0463],\n",
-       "           [ 0.0414, -0.0288,  0.0421],\n",
-       "           [ 0.0324,  0.0388, -0.0529]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([1.0396, 0.9831, 1.0054, 0.9955, 0.9956, 0.9720, 0.9729, 0.9857, 0.9896,\n",
-       "         0.9466, 1.0051, 1.0028, 0.9664, 0.9737, 0.9934, 0.9953, 1.0306, 0.9695,\n",
-       "         1.0003, 0.9727, 0.9750, 0.9908, 1.0377, 1.0163, 1.0164, 0.9739, 0.9609,\n",
-       "         0.9852, 1.0190, 0.9904, 0.9970, 0.9656, 0.9998, 0.9971, 0.9941, 1.0070,\n",
-       "         0.9616, 0.9948, 1.0071, 0.9949, 0.9903, 1.0077, 0.9892, 0.9687, 0.9754,\n",
-       "         0.9935, 0.9794, 0.9938, 0.9885, 0.9935, 0.9825, 1.0078, 0.9843, 0.9761,\n",
-       "         0.9757, 0.9938, 0.9904, 1.0045, 0.9914, 0.9986, 0.9754, 1.0087, 0.9957,\n",
-       "         0.9971, 1.0113, 0.9705, 0.9673, 1.0015, 0.9994, 0.9934, 0.9812, 1.0024,\n",
-       "         0.9845, 0.9740, 1.0137, 0.9988, 1.0049, 0.9993, 0.9990, 0.9793, 1.0180,\n",
-       "         0.9923, 0.9999, 0.9976, 0.9953, 0.9951, 0.9457, 1.0040, 1.0214, 1.0154,\n",
-       "         1.0172, 0.9478, 1.0009, 0.9813, 0.9894, 0.9597, 0.9670, 1.0019, 0.9597,\n",
-       "         0.9610, 1.0005, 1.0257, 0.9999, 0.9906, 1.0055, 0.9657, 0.9579, 0.9858,\n",
-       "         0.9877, 0.9993, 1.0163, 0.9540, 1.0186, 0.9852, 0.9815, 0.9853, 0.9844,\n",
-       "         0.9946, 0.9968, 0.9804, 0.9956, 0.9988, 0.9969, 0.9762, 0.9788, 0.9878,\n",
-       "         0.9749, 0.9585], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.0291, -0.0083, -0.0165,  0.0044, -0.0224, -0.0371, -0.0145,  0.0076,\n",
-       "         -0.0133, -0.0317,  0.0207,  0.0293, -0.0078, -0.0132, -0.0058, -0.0348,\n",
-       "          0.0070, -0.0071,  0.0143, -0.0369, -0.0074, -0.0199,  0.0071, -0.0253,\n",
-       "         -0.0013, -0.0193, -0.0231,  0.0139,  0.0060, -0.0163,  0.0040, -0.0039,\n",
-       "          0.0246, -0.0128, -0.0106,  0.0074, -0.0134, -0.0027, -0.0427, -0.0213,\n",
-       "         -0.0136,  0.0041, -0.0317, -0.0040, -0.0422, -0.0215, -0.0179,  0.0119,\n",
-       "         -0.0106,  0.0167, -0.0074, -0.0245, -0.0393, -0.0164, -0.0293, -0.0202,\n",
-       "         -0.0237, -0.0047, -0.0084, -0.0256, -0.0013, -0.0077, -0.0533, -0.0294,\n",
-       "          0.0113, -0.0256, -0.0229,  0.0135,  0.0152, -0.0403,  0.0055,  0.0226,\n",
-       "         -0.0111, -0.0180,  0.0071,  0.0052,  0.0235, -0.0044, -0.0024, -0.0085,\n",
-       "         -0.0095, -0.0137,  0.0042, -0.0115,  0.0006,  0.0252, -0.0366,  0.0090,\n",
-       "          0.0066,  0.0253,  0.0062, -0.0149, -0.0237, -0.0213,  0.0163, -0.0069,\n",
-       "         -0.0259,  0.0016,  0.0121, -0.0187,  0.0102, -0.0107, -0.0225, -0.0177,\n",
-       "         -0.0024, -0.0003, -0.0400, -0.0291, -0.0242, -0.0065,  0.0136, -0.0277,\n",
-       "          0.0050,  0.0104, -0.0129, -0.0398, -0.0141,  0.0133,  0.0100, -0.0266,\n",
-       "         -0.0175,  0.0287,  0.0110, -0.0188,  0.0099,  0.0181, -0.0138, -0.0180],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 0.0185,  0.0171,  0.0072],\n",
-       "           [-0.0091, -0.0180,  0.0073],\n",
-       "           [ 0.0376, -0.0148, -0.0184]],\n",
-       " \n",
-       "          [[-0.0596, -0.0545, -0.0210],\n",
-       "           [-0.0179, -0.0329, -0.0336],\n",
-       "           [-0.0531, -0.0071, -0.0332]],\n",
-       " \n",
-       "          [[-0.0248, -0.0256, -0.0508],\n",
-       "           [-0.0307,  0.0381, -0.0224],\n",
-       "           [ 0.0198, -0.0118,  0.0421]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0210,  0.0120,  0.0339],\n",
-       "           [-0.0158,  0.0071,  0.0308],\n",
-       "           [ 0.0055,  0.0568,  0.0013]],\n",
-       " \n",
-       "          [[-0.0252, -0.0123, -0.0220],\n",
-       "           [-0.0242, -0.0467, -0.0426],\n",
-       "           [-0.0484, -0.0197, -0.0396]],\n",
-       " \n",
-       "          [[-0.0143, -0.0425, -0.0518],\n",
-       "           [-0.0537, -0.0494, -0.0366],\n",
-       "           [-0.0150,  0.0395, -0.0041]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0145, -0.0249, -0.0418],\n",
-       "           [ 0.0162,  0.0517,  0.0365],\n",
-       "           [-0.0461, -0.0186,  0.0215]],\n",
-       " \n",
-       "          [[-0.0362,  0.0040,  0.0153],\n",
-       "           [-0.0328,  0.0175,  0.0068],\n",
-       "           [ 0.0608,  0.0134,  0.0744]],\n",
-       " \n",
-       "          [[-0.0311,  0.0335,  0.0336],\n",
-       "           [ 0.0287,  0.0285, -0.0422],\n",
-       "           [-0.0724, -0.0289, -0.0280]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0044, -0.0084,  0.0256],\n",
-       "           [ 0.0214, -0.0216, -0.0809],\n",
-       "           [-0.0551, -0.0596, -0.0152]],\n",
-       " \n",
-       "          [[-0.0034,  0.0266, -0.0335],\n",
-       "           [ 0.0009, -0.0535,  0.0065],\n",
-       "           [-0.0494, -0.0325, -0.0141]],\n",
-       " \n",
-       "          [[-0.0067,  0.0220, -0.0023],\n",
-       "           [ 0.0417,  0.0413,  0.0173],\n",
-       "           [-0.0165,  0.0093,  0.0061]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0190, -0.0004, -0.0466],\n",
-       "           [-0.0649, -0.0056, -0.0576],\n",
-       "           [-0.0559, -0.0612, -0.0910]],\n",
-       " \n",
-       "          [[ 0.0330,  0.0002,  0.0475],\n",
-       "           [-0.0098,  0.0599,  0.0567],\n",
-       "           [ 0.0433, -0.0055, -0.0171]],\n",
-       " \n",
-       "          [[ 0.0499,  0.0489, -0.0421],\n",
-       "           [-0.0144,  0.0544,  0.0141],\n",
-       "           [ 0.0328, -0.0037, -0.0195]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0746, -0.0426,  0.0352],\n",
-       "           [-0.0507,  0.0181,  0.0014],\n",
-       "           [ 0.0373, -0.0401,  0.0122]],\n",
-       " \n",
-       "          [[-0.0638, -0.0205, -0.0564],\n",
-       "           [ 0.0163, -0.0611, -0.0271],\n",
-       "           [ 0.0433,  0.0108,  0.0070]],\n",
-       " \n",
-       "          [[ 0.0398,  0.0414, -0.0292],\n",
-       "           [ 0.0136,  0.0409,  0.0159],\n",
-       "           [ 0.0732,  0.0207,  0.0284]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-0.0122, -0.0332, -0.0076],\n",
-       "           [-0.0318, -0.0612, -0.0084],\n",
-       "           [ 0.0154, -0.0536,  0.0379]],\n",
-       " \n",
-       "          [[ 0.0201,  0.0365,  0.0544],\n",
-       "           [-0.0548, -0.0551, -0.0279],\n",
-       "           [ 0.0140, -0.0095, -0.0478]],\n",
-       " \n",
-       "          [[-0.0181,  0.0445,  0.0146],\n",
-       "           [-0.0502,  0.0465,  0.0513],\n",
-       "           [ 0.0444, -0.0069, -0.0024]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0305, -0.0255, -0.0076],\n",
-       "           [ 0.0406, -0.0596, -0.0274],\n",
-       "           [ 0.0115,  0.0203, -0.0143]],\n",
-       " \n",
-       "          [[-0.0222,  0.0273, -0.0047],\n",
-       "           [ 0.0110,  0.0471, -0.0047],\n",
-       "           [-0.0010,  0.0049,  0.0530]],\n",
-       " \n",
-       "          [[-0.0318, -0.0170, -0.0306],\n",
-       "           [-0.0078,  0.0139, -0.0172],\n",
-       "           [-0.0169, -0.0098, -0.0699]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0507,  0.0132, -0.0590],\n",
-       "           [-0.0332,  0.0342,  0.0338],\n",
-       "           [ 0.0258, -0.0137,  0.0480]],\n",
-       " \n",
-       "          [[ 0.0388, -0.0071, -0.0076],\n",
-       "           [ 0.0595, -0.0212,  0.0103],\n",
-       "           [-0.0034, -0.0165,  0.0085]],\n",
-       " \n",
-       "          [[ 0.0159,  0.0327,  0.0182],\n",
-       "           [ 0.0553, -0.0323, -0.0419],\n",
-       "           [ 0.0011,  0.0638, -0.0443]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0220, -0.0045, -0.0433],\n",
-       "           [ 0.0619, -0.0381, -0.0091],\n",
-       "           [ 0.0398, -0.0192, -0.0490]],\n",
-       " \n",
-       "          [[-0.0052, -0.0294, -0.0642],\n",
-       "           [ 0.0432, -0.0454, -0.0175],\n",
-       "           [ 0.0387,  0.0082,  0.0276]],\n",
-       " \n",
-       "          [[ 0.0323,  0.0416,  0.0222],\n",
-       "           [-0.0196, -0.0054, -0.0463],\n",
-       "           [ 0.0403, -0.0234,  0.0378]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0485,  0.0216, -0.0691],\n",
-       "           [-0.0707, -0.0206, -0.0100],\n",
-       "           [-0.0734,  0.0070,  0.0152]],\n",
-       " \n",
-       "          [[-0.0169, -0.0162, -0.0034],\n",
-       "           [ 0.0353,  0.0080, -0.0187],\n",
-       "           [ 0.0093, -0.0518,  0.0300]],\n",
-       " \n",
-       "          [[-0.0459,  0.0050, -0.0120],\n",
-       "           [ 0.0332, -0.0316, -0.0345],\n",
-       "           [-0.0147,  0.0189,  0.0291]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0341, -0.0032,  0.0254],\n",
-       "           [ 0.0434, -0.0074,  0.0601],\n",
-       "           [ 0.0625,  0.0131,  0.0215]],\n",
-       " \n",
-       "          [[ 0.0033, -0.0286,  0.0050],\n",
-       "           [ 0.0624,  0.0415,  0.0407],\n",
-       "           [-0.0107,  0.0621,  0.0122]],\n",
-       " \n",
-       "          [[ 0.0589, -0.0486, -0.0085],\n",
-       "           [ 0.0140, -0.0526, -0.0349],\n",
-       "           [-0.0325,  0.0424, -0.0454]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([1.0406, 1.0325, 0.9970, 0.9878, 1.0117, 1.0056, 1.0172, 0.9924, 0.9937,\n",
-       "         1.0335, 0.9728, 1.0054, 1.0281, 1.0054, 1.0086, 0.9706, 0.9934, 0.9744,\n",
-       "         1.0006, 1.0106, 1.0037, 0.9679, 1.0211, 0.9921, 0.9928, 1.0094, 1.0172,\n",
-       "         0.9927, 1.0329, 0.9801, 1.0035, 1.0113, 0.9672, 1.0122, 1.0348, 0.9953,\n",
-       "         0.9861, 0.9959, 1.0017, 0.9874, 0.9958, 1.0000, 0.9946, 0.9954, 0.9942,\n",
-       "         0.9815, 0.9846, 0.9843, 1.0032, 0.9791, 0.9744, 0.9934, 0.9685, 1.0049,\n",
-       "         0.9668, 0.9965, 1.0099, 0.9907, 0.9629, 1.0054, 0.9877, 1.0137, 0.9923,\n",
-       "         1.0043], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 6.6964e-02, -2.3313e-02, -5.0823e-03, -1.0087e-02,  2.6752e-02,\n",
-       "         -1.7251e-02, -6.5404e-03, -3.1139e-05, -5.7620e-04,  2.5207e-02,\n",
-       "         -4.4233e-02,  1.0115e-02, -2.2741e-02, -3.2897e-02,  7.8232e-03,\n",
-       "         -1.4690e-02, -5.8536e-04, -9.5899e-03, -3.1390e-02,  4.1087e-02,\n",
-       "         -4.6631e-02, -3.0345e-02, -2.8544e-02, -8.1191e-03,  1.6275e-02,\n",
-       "          3.5236e-04,  5.2836e-03, -1.8740e-02,  7.1831e-02, -1.1400e-02,\n",
-       "          2.5948e-03, -6.3489e-03, -3.7302e-02,  9.9579e-03,  3.7794e-02,\n",
-       "          1.0596e-03, -4.0767e-02, -4.0590e-02,  4.9663e-02, -3.2993e-02,\n",
-       "         -4.3150e-02, -2.1281e-02, -1.9469e-02, -4.8431e-03,  9.2445e-03,\n",
-       "         -1.0817e-02, -5.9809e-03, -3.2433e-02,  5.9403e-03, -3.3776e-02,\n",
-       "         -1.3215e-02, -3.9797e-02, -2.8940e-02, -4.2454e-03, -8.2416e-02,\n",
-       "          1.0369e-02, -3.7708e-03, -1.3592e-02, -1.7106e-02, -1.8426e-02,\n",
-       "         -1.7556e-02,  2.5320e-02, -3.0962e-02,  3.6128e-02], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 0.0560,  0.0242,  0.0232],\n",
-       "           [-0.0004,  0.0376, -0.1013],\n",
-       "           [-0.1007,  0.0121,  0.0763]],\n",
-       " \n",
-       "          [[ 0.0437,  0.0786, -0.0674],\n",
-       "           [-0.0313, -0.0627, -0.0974],\n",
-       "           [-0.0920, -0.0472,  0.0113]],\n",
-       " \n",
-       "          [[-0.0389,  0.0794,  0.0297],\n",
-       "           [-0.0273,  0.0437, -0.0561],\n",
-       "           [-0.0127, -0.0155, -0.0708]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0577,  0.0858, -0.0668],\n",
-       "           [-0.0591,  0.0726, -0.0031],\n",
-       "           [ 0.1141, -0.0934, -0.0075]],\n",
-       " \n",
-       "          [[-0.0432,  0.0118,  0.0831],\n",
-       "           [-0.0878, -0.0886, -0.0120],\n",
-       "           [ 0.0917, -0.0068,  0.0307]],\n",
-       " \n",
-       "          [[-0.0382,  0.0431,  0.0831],\n",
-       "           [-0.0702,  0.0499,  0.1028],\n",
-       "           [-0.0726,  0.0495,  0.0230]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0003,  0.0367, -0.0693],\n",
-       "           [-0.0585, -0.0577, -0.0111],\n",
-       "           [-0.0444, -0.0354,  0.0760]],\n",
-       " \n",
-       "          [[ 0.1210, -0.0473,  0.0012],\n",
-       "           [ 0.0092, -0.0308,  0.1076],\n",
-       "           [-0.0732,  0.0227, -0.0699]],\n",
-       " \n",
-       "          [[-0.0679, -0.0471, -0.0506],\n",
-       "           [ 0.0145, -0.0691,  0.1080],\n",
-       "           [-0.0801, -0.0496, -0.0322]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0529, -0.0365,  0.0822],\n",
-       "           [-0.0100,  0.0062,  0.0580],\n",
-       "           [ 0.0051, -0.0318, -0.0157]],\n",
-       " \n",
-       "          [[-0.0739,  0.0855, -0.0857],\n",
-       "           [-0.0162,  0.0366, -0.0575],\n",
-       "           [ 0.0811,  0.0196,  0.1065]],\n",
-       " \n",
-       "          [[ 0.0553, -0.0750, -0.0866],\n",
-       "           [-0.0500, -0.0763, -0.0153],\n",
-       "           [-0.0893,  0.0399,  0.0681]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0218,  0.0857, -0.0787],\n",
-       "           [-0.1086,  0.0782, -0.0356],\n",
-       "           [ 0.0679, -0.0123, -0.0253]],\n",
-       " \n",
-       "          [[ 0.1062, -0.0310,  0.0572],\n",
-       "           [ 0.0818, -0.0548,  0.0202],\n",
-       "           [ 0.0845,  0.0831,  0.0677]],\n",
-       " \n",
-       "          [[-0.0778,  0.0137, -0.0147],\n",
-       "           [-0.0291,  0.0807,  0.0937],\n",
-       "           [-0.0122, -0.0161, -0.0611]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0355,  0.0102,  0.0300],\n",
-       "           [-0.0689, -0.0960,  0.0287],\n",
-       "           [-0.0780, -0.0464,  0.0537]],\n",
-       " \n",
-       "          [[ 0.0394, -0.0330, -0.0008],\n",
-       "           [ 0.1171, -0.0284,  0.1019],\n",
-       "           [-0.0512, -0.0247, -0.0007]],\n",
-       " \n",
-       "          [[-0.0282, -0.0229,  0.0334],\n",
-       "           [ 0.0655,  0.0271, -0.0579],\n",
-       "           [-0.0087,  0.0127, -0.0637]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0296, -0.0017, -0.0179],\n",
-       "           [ 0.0728, -0.0400, -0.0364],\n",
-       "           [ 0.0278,  0.0478, -0.0480]],\n",
-       " \n",
-       "          [[-0.0778,  0.0320, -0.0375],\n",
-       "           [-0.0096, -0.0833, -0.0204],\n",
-       "           [ 0.0163, -0.0936, -0.1035]],\n",
-       " \n",
-       "          [[-0.0017,  0.0117,  0.0633],\n",
-       "           [ 0.0660,  0.0982, -0.0034],\n",
-       "           [ 0.0589,  0.0244,  0.0125]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0457, -0.0317,  0.0376],\n",
-       "           [-0.0043, -0.0150,  0.0670],\n",
-       "           [-0.0313,  0.0712,  0.0213]],\n",
-       " \n",
-       "          [[-0.0830, -0.0063, -0.0141],\n",
-       "           [ 0.0059, -0.0471,  0.0355],\n",
-       "           [-0.0462,  0.0807,  0.0664]],\n",
-       " \n",
-       "          [[-0.0922, -0.0426, -0.0035],\n",
-       "           [ 0.0165, -0.0907,  0.0381],\n",
-       "           [ 0.0889, -0.0643, -0.0512]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0019, -0.0600,  0.0895],\n",
-       "           [ 0.1060,  0.0684,  0.0104],\n",
-       "           [ 0.0608, -0.0410,  0.1043]],\n",
-       " \n",
-       "          [[ 0.0694, -0.0240, -0.1017],\n",
-       "           [ 0.0837, -0.0013, -0.0769],\n",
-       "           [ 0.0235,  0.0082,  0.0159]],\n",
-       " \n",
-       "          [[-0.0887,  0.0610,  0.0451],\n",
-       "           [ 0.1016,  0.0097, -0.0382],\n",
-       "           [ 0.0773, -0.0648, -0.0456]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0372,  0.0156, -0.0305],\n",
-       "           [-0.1074, -0.0103,  0.0696],\n",
-       "           [-0.0283,  0.0407,  0.0306]],\n",
-       " \n",
-       "          [[ 0.0483, -0.0867, -0.0932],\n",
-       "           [ 0.0400, -0.0486, -0.0254],\n",
-       "           [-0.0138, -0.0525, -0.0361]],\n",
-       " \n",
-       "          [[ 0.0359,  0.0337,  0.0703],\n",
-       "           [-0.0396, -0.0174,  0.0487],\n",
-       "           [-0.0385,  0.0209, -0.0562]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0592, -0.0949, -0.1108],\n",
-       "           [ 0.0573, -0.0056,  0.0354],\n",
-       "           [ 0.0226,  0.0442, -0.0982]],\n",
-       " \n",
-       "          [[-0.0072,  0.0984,  0.0688],\n",
-       "           [ 0.0116,  0.1153, -0.0401],\n",
-       "           [-0.0542, -0.0358,  0.0757]],\n",
-       " \n",
-       "          [[-0.0116, -0.0223,  0.0258],\n",
-       "           [ 0.0255,  0.0703, -0.0460],\n",
-       "           [ 0.0605,  0.0881,  0.0198]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0720, -0.0654, -0.0269],\n",
-       "           [-0.0276,  0.0297, -0.0548],\n",
-       "           [-0.0570,  0.0281,  0.0246]],\n",
-       " \n",
-       "          [[-0.0683,  0.0208,  0.0504],\n",
-       "           [ 0.0903,  0.0710, -0.0267],\n",
-       "           [ 0.0630, -0.0195, -0.0110]],\n",
-       " \n",
-       "          [[ 0.0208,  0.0575,  0.0353],\n",
-       "           [-0.0499, -0.0339, -0.0153],\n",
-       "           [-0.0998,  0.0691, -0.1154]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.9947, 0.9445, 0.9648, 1.0078, 0.9852, 0.9980, 1.0174, 1.0136, 0.9618,\n",
-       "         0.9430, 0.9716, 0.9886, 0.9754, 0.9848, 0.9673, 0.9358, 0.9801, 0.9711,\n",
-       "         0.9637, 0.9817, 0.9768, 0.9501, 0.9832, 0.9720, 1.0085, 0.9520, 0.9939,\n",
-       "         0.9635, 0.9919, 0.9680, 0.9748, 1.0541, 1.0251, 0.9883, 0.9983, 0.9814,\n",
-       "         0.9685, 0.9890, 0.9944, 0.9797, 1.0295, 0.9891, 0.9516, 0.9478, 0.9511,\n",
-       "         0.9944, 1.0212, 0.9823, 0.9774, 1.0114, 0.9900, 0.9936, 0.9898, 0.9753,\n",
-       "         1.0081, 0.9818, 0.9893, 0.9580, 0.9547, 0.9794, 1.0214, 0.9807, 0.9444,\n",
-       "         0.9920], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.0081, -0.0575, -0.0405, -0.0533, -0.0338, -0.0380,  0.0289,  0.0634,\n",
-       "         -0.0415, -0.0464,  0.0153, -0.0322, -0.0104,  0.0044, -0.0368, -0.0506,\n",
-       "         -0.0116, -0.0337, -0.0343, -0.0617, -0.0090, -0.0416,  0.0012, -0.0542,\n",
-       "          0.0191, -0.0029, -0.0164, -0.0181,  0.0132, -0.0497, -0.0426,  0.0409,\n",
-       "          0.0310,  0.0052, -0.0390, -0.0144, -0.0075,  0.0086,  0.0171, -0.0339,\n",
-       "         -0.0100, -0.0284, -0.0157, -0.0458, -0.0219, -0.0426,  0.0310, -0.0147,\n",
-       "          0.0153,  0.0366, -0.0110, -0.0590, -0.0106, -0.0362,  0.0170, -0.0193,\n",
-       "         -0.0193, -0.0157, -0.0112, -0.0080,  0.0370, -0.0202, -0.0117, -0.0574],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-1.2035e-02, -2.2200e-02, -5.0168e-02],\n",
-       "           [-1.0694e-01, -9.7930e-02,  5.9749e-03],\n",
-       "           [ 9.3646e-03, -5.5568e-02,  1.2322e-02]],\n",
-       " \n",
-       "          [[-8.6864e-02, -3.3802e-02, -3.8302e-02],\n",
-       "           [-7.1682e-03,  4.2829e-02, -4.0924e-02],\n",
-       "           [-5.2170e-02, -5.1692e-02,  4.6925e-02]],\n",
-       " \n",
-       "          [[-9.5456e-02, -6.2303e-02, -1.3622e-02],\n",
-       "           [-1.7523e-02,  4.7278e-02, -7.2578e-03],\n",
-       "           [-7.9575e-02, -8.6043e-02, -5.0468e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 7.5643e-03, -1.3133e-02, -3.0743e-02],\n",
-       "           [ 4.9852e-02, -4.1459e-02,  3.6311e-02],\n",
-       "           [ 1.8915e-02, -5.6099e-02, -3.2455e-03]],\n",
-       " \n",
-       "          [[-4.1142e-03,  7.2733e-02,  3.1645e-02],\n",
-       "           [ 7.2735e-02, -2.4539e-02, -4.4924e-02],\n",
-       "           [-2.5447e-02, -6.1464e-02, -2.2168e-02]],\n",
-       " \n",
-       "          [[-1.8502e-02,  3.9506e-02, -2.0897e-02],\n",
-       "           [ 5.1837e-02, -2.3057e-03,  9.1045e-03],\n",
-       "           [-6.8957e-02,  6.7414e-02,  6.6131e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 6.4568e-03,  3.8176e-02,  6.5913e-02],\n",
-       "           [ 3.0586e-03, -2.8019e-03,  1.3615e-02],\n",
-       "           [ 3.6079e-02, -2.1676e-02,  4.9147e-02]],\n",
-       " \n",
-       "          [[-5.9103e-02, -6.6769e-02,  6.8484e-02],\n",
-       "           [ 5.6405e-02,  2.9459e-02,  3.3922e-02],\n",
-       "           [-1.4241e-02, -5.5273e-02,  6.0410e-02]],\n",
-       " \n",
-       "          [[-6.1112e-02,  4.2447e-02,  7.0788e-02],\n",
-       "           [ 4.0645e-02,  2.5614e-02,  1.5963e-02],\n",
-       "           [ 7.5878e-02,  5.3478e-02,  6.8040e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-6.0853e-02, -4.0503e-02, -6.5746e-02],\n",
-       "           [-3.8512e-02, -7.0689e-02, -4.5878e-02],\n",
-       "           [-5.0548e-03, -8.5858e-03, -1.3259e-02]],\n",
-       " \n",
-       "          [[-3.2387e-02,  6.8637e-02, -8.4986e-03],\n",
-       "           [-5.8194e-02, -4.1972e-02,  6.8163e-02],\n",
-       "           [-5.2408e-03, -5.4924e-02,  3.3050e-02]],\n",
-       " \n",
-       "          [[ 5.1264e-02, -4.3981e-02, -6.4656e-02],\n",
-       "           [ 6.1986e-02, -6.3685e-02,  2.9124e-03],\n",
-       "           [-3.6842e-02, -1.3093e-02,  4.2239e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-2.2500e-02, -8.3170e-03, -3.5613e-02],\n",
-       "           [-3.6937e-02, -8.2547e-02, -1.7730e-03],\n",
-       "           [ 4.2401e-02,  4.9475e-02,  1.7827e-02]],\n",
-       " \n",
-       "          [[ 1.4852e-02, -5.8275e-02, -2.1544e-02],\n",
-       "           [-2.9260e-02,  3.7038e-02, -1.8404e-02],\n",
-       "           [-8.2650e-02,  3.0826e-02,  2.9142e-02]],\n",
-       " \n",
-       "          [[-2.2315e-02, -2.8522e-02,  3.4681e-02],\n",
-       "           [-8.9954e-02,  1.3894e-03,  1.3379e-02],\n",
-       "           [-2.5037e-02, -7.7501e-02,  3.1933e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.8031e-02,  6.7870e-02, -2.6617e-02],\n",
-       "           [-3.4012e-02,  5.9386e-02, -6.3182e-02],\n",
-       "           [ 5.3046e-02,  7.6123e-03, -1.4182e-02]],\n",
-       " \n",
-       "          [[ 5.1025e-02, -6.4731e-02, -4.6948e-04],\n",
-       "           [ 5.0366e-02,  3.9458e-02, -5.7561e-02],\n",
-       "           [ 1.2097e-02,  1.0614e-02, -5.6899e-02]],\n",
-       " \n",
-       "          [[ 6.5736e-03, -1.9818e-02,  2.2680e-02],\n",
-       "           [-6.4852e-02,  4.5996e-02,  1.7559e-02],\n",
-       "           [ 3.3078e-02, -5.2623e-02,  1.2356e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-9.0290e-04,  1.2771e-02, -2.6509e-02],\n",
-       "           [ 7.1526e-03, -5.8129e-02,  4.1453e-02],\n",
-       "           [ 3.7940e-02,  3.3241e-02, -1.6080e-02]],\n",
-       " \n",
-       "          [[ 1.2149e-02,  1.6645e-02,  2.2164e-02],\n",
-       "           [-8.8278e-02,  2.0142e-02,  3.1339e-02],\n",
-       "           [ 1.7679e-02, -9.3592e-03, -7.0434e-02]],\n",
-       " \n",
-       "          [[ 1.9289e-02, -7.8164e-02, -8.9197e-02],\n",
-       "           [ 2.0250e-02, -2.3763e-02, -6.6507e-02],\n",
-       "           [ 3.5052e-02,  2.7825e-02,  3.2383e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 2.0298e-02,  4.0362e-02,  2.9226e-02],\n",
-       "           [ 8.3158e-02,  7.2123e-02, -3.8499e-03],\n",
-       "           [ 7.2112e-02, -3.9205e-03, -3.1428e-02]],\n",
-       " \n",
-       "          [[ 1.1813e-02,  5.3354e-02, -9.0460e-03],\n",
-       "           [ 4.8429e-02, -1.2598e-02,  5.9573e-02],\n",
-       "           [-1.1803e-02, -5.4147e-03, -1.9353e-02]],\n",
-       " \n",
-       "          [[ 5.7091e-02, -4.6743e-02,  4.9557e-02],\n",
-       "           [-2.6745e-02, -3.0528e-02, -4.8659e-02],\n",
-       "           [ 3.1489e-02, -4.3289e-02, -4.1977e-03]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 6.0787e-02,  3.1088e-02, -2.8077e-02],\n",
-       "           [ 3.5500e-02, -1.1130e-02,  5.6940e-02],\n",
-       "           [ 4.7613e-02,  4.7212e-02, -4.0806e-02]],\n",
-       " \n",
-       "          [[ 4.8701e-02, -1.7467e-02,  1.1671e-02],\n",
-       "           [ 2.4514e-02, -2.3033e-03,  3.8036e-02],\n",
-       "           [-2.0489e-02, -5.5674e-02,  4.0689e-02]],\n",
-       " \n",
-       "          [[ 6.5609e-02,  3.7343e-02, -2.8431e-03],\n",
-       "           [ 5.8800e-03,  4.3508e-02,  1.6820e-02],\n",
-       "           [ 4.9291e-02,  5.6582e-02,  2.7121e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 3.1413e-02, -2.9208e-02, -2.7987e-02],\n",
-       "           [-1.6460e-02, -1.7421e-02,  1.9204e-02],\n",
-       "           [-5.1881e-03,  1.8484e-02,  5.7155e-02]],\n",
-       " \n",
-       "          [[ 2.5080e-02,  1.6554e-02, -5.5290e-02],\n",
-       "           [-6.4464e-02, -2.4550e-03,  9.0651e-04],\n",
-       "           [-5.0061e-02, -1.6828e-02,  3.6484e-02]],\n",
-       " \n",
-       "          [[-3.6489e-02, -4.4884e-02,  6.1340e-03],\n",
-       "           [-3.0670e-02,  5.1744e-02,  5.1705e-02],\n",
-       "           [-1.6312e-02,  5.9209e-02, -4.0835e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-4.8251e-02,  3.2003e-03,  2.4275e-02],\n",
-       "           [-7.8823e-02,  8.6544e-03,  3.4735e-02],\n",
-       "           [-7.5714e-02, -8.4469e-02, -9.0082e-02]],\n",
-       " \n",
-       "          [[ 1.2249e-02, -2.4418e-03, -5.6304e-02],\n",
-       "           [ 2.1320e-02,  8.3675e-03, -8.0636e-03],\n",
-       "           [-4.3117e-02,  2.6799e-02,  5.5333e-02]],\n",
-       " \n",
-       "          [[ 6.9515e-02,  2.9099e-02, -5.1356e-02],\n",
-       "           [-7.6398e-03,  9.9813e-04,  4.9508e-02],\n",
-       "           [-2.3900e-02,  6.0849e-02,  3.7288e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 5.4104e-02,  4.2785e-02,  7.0741e-02],\n",
-       "           [-2.8993e-02,  6.8216e-03,  2.3454e-02],\n",
-       "           [-3.5220e-02,  3.5645e-02, -1.4602e-02]],\n",
-       " \n",
-       "          [[-2.9401e-02, -5.2008e-02,  4.9415e-02],\n",
-       "           [-2.7387e-02,  1.1299e-02, -5.5928e-02],\n",
-       "           [ 1.8970e-02, -4.5985e-05, -2.1626e-02]],\n",
-       " \n",
-       "          [[-4.9945e-02, -5.1643e-02,  1.0528e-02],\n",
-       "           [ 5.4602e-03, -3.2667e-02,  5.3188e-02],\n",
-       "           [-6.9626e-02, -3.4165e-02,  1.4758e-02]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.9874, 1.0147, 0.9964, 1.0173, 1.0188, 0.9534, 1.0751, 0.9979, 0.9783,\n",
-       "         1.0361, 0.9957, 1.0121, 0.9725, 1.0181, 1.0137, 0.9865, 0.9830, 1.0378,\n",
-       "         1.0208, 1.0257, 0.9982, 0.9886, 1.0130, 0.9844, 0.9854, 1.0137, 0.9804,\n",
-       "         0.9814, 0.9782, 0.9866, 0.9613, 0.9880], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.0006,  0.0078,  0.0003,  0.0243, -0.0293, -0.0207,  0.0671,  0.0091,\n",
-       "         -0.0737,  0.0777, -0.0320,  0.0049, -0.0366,  0.0028, -0.0451, -0.0177,\n",
-       "         -0.0422,  0.0551,  0.0295, -0.0078, -0.0160, -0.0438,  0.0594, -0.0396,\n",
-       "         -0.0243, -0.0527,  0.0205, -0.0379, -0.0163,  0.0103, -0.0489,  0.0064],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 3.2116e-03, -9.7481e-02, -1.0325e-01],\n",
-       "           [ 1.6994e-02,  3.9613e-02,  1.4053e-02],\n",
-       "           [-8.8826e-02,  3.7712e-02,  1.3518e-02]],\n",
-       " \n",
-       "          [[ 2.5617e-02, -7.3934e-03,  1.4787e-01],\n",
-       "           [ 1.3256e-01,  1.4265e-02,  3.3551e-02],\n",
-       "           [-9.3728e-02,  1.3718e-01,  1.4687e-01]],\n",
-       " \n",
-       "          [[-7.2511e-02,  3.1657e-02, -2.0823e-02],\n",
-       "           [-1.3836e-01,  1.2133e-03,  5.1334e-02],\n",
-       "           [ 1.7416e-02,  4.7993e-02, -5.7910e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.5549e-02, -1.2489e-01, -7.9055e-02],\n",
-       "           [-1.4252e-01, -1.3892e-03,  2.5320e-02],\n",
-       "           [-5.6475e-02,  5.7041e-02,  4.2386e-02]],\n",
-       " \n",
-       "          [[ 1.1459e-01, -7.4020e-02, -8.6425e-02],\n",
-       "           [-6.5544e-02, -6.7253e-02,  5.1786e-02],\n",
-       "           [ 1.2604e-01,  9.3295e-02,  2.9762e-02]],\n",
-       " \n",
-       "          [[ 1.0229e-01,  1.0709e-01,  6.2699e-03],\n",
-       "           [-1.4997e-01,  2.0815e-02,  1.1508e-01],\n",
-       "           [-5.6745e-02, -7.4981e-02,  6.2848e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-9.0134e-02, -5.1571e-02, -1.6902e-03],\n",
-       "           [ 3.2890e-02, -1.4046e-01,  2.1443e-02],\n",
-       "           [ 1.9174e-02, -1.0684e-01, -1.0109e-01]],\n",
-       " \n",
-       "          [[ 2.2483e-02, -1.5111e-01, -5.7259e-02],\n",
-       "           [-5.3119e-06,  5.9270e-02, -8.6450e-03],\n",
-       "           [-2.6963e-02, -8.8765e-02, -6.3491e-02]],\n",
-       " \n",
-       "          [[-6.8200e-02,  1.8680e-02, -2.8912e-02],\n",
-       "           [-1.3314e-01, -4.0874e-02, -9.9302e-02],\n",
-       "           [-1.1984e-01, -1.4110e-01, -9.3381e-03]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 8.7052e-02,  4.3037e-02, -6.1989e-02],\n",
-       "           [ 8.2560e-02,  2.7525e-02, -1.1605e-01],\n",
-       "           [ 8.8720e-02, -6.2233e-03, -6.0589e-02]],\n",
-       " \n",
-       "          [[-9.2557e-02,  1.1975e-01,  2.0139e-02],\n",
-       "           [ 4.3450e-02, -5.6374e-02, -1.1839e-01],\n",
-       "           [-7.8554e-02,  2.0028e-02, -6.4422e-02]],\n",
-       " \n",
-       "          [[-7.5318e-02, -3.3087e-02,  6.1934e-02],\n",
-       "           [-1.9952e-02, -5.7307e-02,  1.0375e-01],\n",
-       "           [ 4.7526e-02, -3.0824e-02,  3.5812e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 1.4232e-01, -6.0943e-02,  4.2219e-02],\n",
-       "           [ 7.1065e-02,  1.4855e-02, -8.4384e-02],\n",
-       "           [ 1.2598e-01,  8.6912e-02,  1.2212e-02]],\n",
-       " \n",
-       "          [[-4.8087e-03,  8.7954e-02,  2.8662e-02],\n",
-       "           [-7.2713e-02,  1.0243e-01,  5.6573e-03],\n",
-       "           [ 1.2547e-02, -4.7541e-02,  3.0690e-02]],\n",
-       " \n",
-       "          [[-4.0232e-02,  4.3232e-02,  3.3061e-02],\n",
-       "           [ 8.9064e-03, -2.0085e-02, -5.6324e-02],\n",
-       "           [-4.9874e-02,  1.4376e-02, -1.9317e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 1.6703e-02, -8.8358e-03,  8.0942e-02],\n",
-       "           [-5.3988e-02,  1.3815e-01,  1.3562e-01],\n",
-       "           [-9.1911e-03,  1.4084e-01,  1.1233e-01]],\n",
-       " \n",
-       "          [[ 1.2753e-01,  7.4018e-04, -5.3322e-03],\n",
-       "           [ 7.6872e-02,  8.9114e-03,  7.5354e-02],\n",
-       "           [ 9.4679e-02,  2.2750e-02, -1.3565e-02]],\n",
-       " \n",
-       "          [[ 9.4207e-02,  2.6639e-02,  1.2351e-01],\n",
-       "           [ 1.1471e-01, -1.2649e-01,  8.6726e-02],\n",
-       "           [-6.4728e-02,  1.1735e-01, -6.1134e-02]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-9.8098e-02, -5.7065e-02, -1.0018e-02],\n",
-       "           [-5.8279e-02,  1.4330e-01, -3.4766e-02],\n",
-       "           [ 2.2782e-02, -1.1115e-01,  6.3341e-02]],\n",
-       " \n",
-       "          [[-2.0408e-02,  1.0746e-01,  1.7395e-02],\n",
-       "           [-1.1585e-01, -1.0155e-01,  1.1387e-02],\n",
-       "           [ 5.1462e-02, -1.4018e-01,  2.3420e-02]],\n",
-       " \n",
-       "          [[-1.7346e-03, -7.2362e-02,  5.5024e-03],\n",
-       "           [-8.8688e-02, -1.8622e-03, -1.5808e-01],\n",
-       "           [ 2.4130e-02, -7.2761e-02,  7.4067e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-2.4241e-02, -4.5848e-02,  2.0990e-02],\n",
-       "           [ 6.1874e-02, -8.1900e-02, -7.6938e-02],\n",
-       "           [-4.5193e-02, -4.4950e-02, -1.2600e-01]],\n",
-       " \n",
-       "          [[ 9.8236e-02,  6.5378e-02, -8.5482e-02],\n",
-       "           [-8.3849e-02, -8.9709e-02, -1.3068e-01],\n",
-       "           [-6.7682e-02, -1.3581e-01,  4.2147e-02]],\n",
-       " \n",
-       "          [[-1.4547e-01, -2.6404e-02,  1.3460e-02],\n",
-       "           [ 1.5150e-02,  8.2466e-02, -4.1508e-02],\n",
-       "           [ 9.6086e-03,  9.5105e-02,  1.2398e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[-7.5055e-02, -2.7225e-03, -2.4004e-02],\n",
-       "           [-1.2743e-01, -3.6428e-02,  9.0063e-02],\n",
-       "           [-4.2032e-02,  9.7788e-03,  7.3457e-02]],\n",
-       " \n",
-       "          [[ 4.4698e-02, -3.0341e-02,  6.5091e-03],\n",
-       "           [-2.8412e-02,  4.6901e-02, -8.7063e-02],\n",
-       "           [ 9.7245e-02,  6.5506e-02,  7.7619e-02]],\n",
-       " \n",
-       "          [[-3.6357e-02, -5.6557e-02,  1.1905e-01],\n",
-       "           [ 8.6448e-02,  1.1533e-01, -7.5111e-02],\n",
-       "           [-2.5771e-02, -1.4157e-01, -1.4335e-02]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-6.9808e-02,  5.7288e-02,  8.1909e-02],\n",
-       "           [ 3.7587e-03, -9.1612e-02, -9.5226e-02],\n",
-       "           [-5.9170e-02, -8.8632e-02, -4.5599e-03]],\n",
-       " \n",
-       "          [[ 1.5217e-01, -1.2436e-01,  9.9563e-02],\n",
-       "           [-1.1019e-01, -8.7462e-02, -2.6099e-02],\n",
-       "           [-7.8914e-02, -7.4303e-02,  1.4454e-01]],\n",
-       " \n",
-       "          [[-1.0211e-01, -4.2046e-02, -1.0236e-01],\n",
-       "           [ 9.0761e-02, -1.3478e-01, -1.0698e-01],\n",
-       "           [-8.0105e-02,  7.3618e-02, -6.2710e-02]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 3.8851e-02,  5.7854e-02, -6.1053e-02],\n",
-       "           [ 9.2899e-03,  1.2101e-01,  7.6231e-02],\n",
-       "           [-4.6224e-02, -3.6402e-02,  1.8597e-02]],\n",
-       " \n",
-       "          [[ 1.1156e-01,  9.9315e-02,  7.3216e-02],\n",
-       "           [ 6.5939e-02, -1.2361e-01, -4.2214e-02],\n",
-       "           [-2.5123e-02,  2.0662e-02, -9.9345e-03]],\n",
-       " \n",
-       "          [[ 8.9013e-02,  8.9467e-02,  6.8107e-02],\n",
-       "           [-2.6640e-02, -7.1242e-03,  1.4669e-01],\n",
-       "           [ 9.4380e-02, -2.5333e-02,  1.6211e-01]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-1.3300e-01, -9.8155e-02,  1.6880e-02],\n",
-       "           [-1.1402e-01, -1.3630e-01,  9.4534e-03],\n",
-       "           [-1.1893e-01,  1.2817e-01,  7.7060e-02]],\n",
-       " \n",
-       "          [[-1.1836e-01,  9.6878e-02, -7.9946e-02],\n",
-       "           [-1.2008e-01,  1.2003e-01, -1.1213e-01],\n",
-       "           [ 1.8616e-03, -1.3560e-01,  7.9426e-02]],\n",
-       " \n",
-       "          [[-4.9415e-02,  4.7683e-02,  1.9771e-02],\n",
-       "           [-7.5200e-02, -2.3387e-02,  6.4178e-02],\n",
-       "           [-8.3820e-02,  8.9533e-03,  1.1820e-01]]]], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.9955, 1.0600, 0.9922, 1.0291, 1.0538, 0.9873, 0.9750, 1.0177, 0.9947,\n",
-       "         0.9761, 0.9635, 0.9608, 0.9893, 0.9813, 0.9793, 0.9686, 0.9846, 0.9968,\n",
-       "         1.0611, 0.9983, 1.0147, 1.0005, 0.9783, 0.9945, 1.0058, 0.9694, 0.9854,\n",
-       "         1.0451, 1.0022, 1.0115, 0.9859, 0.9732], device='cuda:0',\n",
-       "        requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.0083,  0.0469, -0.0105,  0.0363,  0.0457, -0.0573, -0.0112,  0.0077,\n",
-       "         -0.0173, -0.0552, -0.0329, -0.0438, -0.0073, -0.0364, -0.0085, -0.0060,\n",
-       "         -0.0232, -0.0192,  0.0481,  0.0085, -0.0377,  0.0093, -0.0136,  0.0055,\n",
-       "         -0.0471, -0.0528, -0.0844,  0.0185, -0.0359, -0.0003, -0.0273, -0.0203],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-0.0641, -0.1102, -0.0002],\n",
-       "           [-0.1472, -0.1502,  0.0386],\n",
-       "           [-0.0050, -0.0699, -0.0964]],\n",
-       " \n",
-       "          [[ 0.0894,  0.0491,  0.1342],\n",
-       "           [ 0.1380, -0.1265,  0.0175],\n",
-       "           [ 0.0084,  0.0843, -0.0607]],\n",
-       " \n",
-       "          [[ 0.1088, -0.0510,  0.0032],\n",
-       "           [-0.1108, -0.1247, -0.0186],\n",
-       "           [-0.0222,  0.0713, -0.1057]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.1236, -0.0428,  0.0245],\n",
-       "           [ 0.0898,  0.0454, -0.0278],\n",
-       "           [-0.1081, -0.1273,  0.1334]],\n",
-       " \n",
-       "          [[ 0.0580,  0.0842,  0.0753],\n",
-       "           [ 0.0284, -0.1006,  0.0061],\n",
-       "           [ 0.0531, -0.0633, -0.0623]],\n",
-       " \n",
-       "          [[-0.0149,  0.0028,  0.0660],\n",
-       "           [ 0.0056,  0.1160,  0.1148],\n",
-       "           [-0.0147,  0.0404, -0.0987]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0159, -0.0408,  0.0718],\n",
-       "           [ 0.0465,  0.0409,  0.0628],\n",
-       "           [ 0.1284,  0.0349, -0.0306]],\n",
-       " \n",
-       "          [[-0.0448, -0.1009, -0.1298],\n",
-       "           [-0.0972, -0.1393, -0.1648],\n",
-       "           [-0.0937,  0.0046, -0.0041]],\n",
-       " \n",
-       "          [[ 0.1310, -0.0050,  0.0486],\n",
-       "           [ 0.1046,  0.0764, -0.0594],\n",
-       "           [-0.1388,  0.1170, -0.0904]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.1283, -0.0046, -0.1166],\n",
-       "           [ 0.1103,  0.0176,  0.0997],\n",
-       "           [ 0.0440, -0.1119, -0.0031]],\n",
-       " \n",
-       "          [[-0.0196,  0.0673, -0.0781],\n",
-       "           [ 0.0341,  0.0408,  0.0176],\n",
-       "           [ 0.0024,  0.0762, -0.0757]],\n",
-       " \n",
-       "          [[-0.0456,  0.0358, -0.0119],\n",
-       "           [-0.0663, -0.0787,  0.0489],\n",
-       "           [ 0.0671, -0.0691, -0.1025]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0471,  0.0339,  0.0042],\n",
-       "           [-0.0995, -0.0108,  0.1195],\n",
-       "           [ 0.0076, -0.0739,  0.1120]],\n",
-       " \n",
-       "          [[-0.0040, -0.0884, -0.1344],\n",
-       "           [ 0.0908, -0.0947,  0.0684],\n",
-       "           [-0.1537,  0.0935,  0.0262]],\n",
-       " \n",
-       "          [[ 0.1077, -0.1229,  0.0766],\n",
-       "           [-0.0561,  0.0231,  0.0808],\n",
-       "           [ 0.0966,  0.1160,  0.1313]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0355,  0.0112, -0.1081],\n",
-       "           [ 0.0093,  0.1012, -0.0729],\n",
-       "           [ 0.0338, -0.0075,  0.1268]],\n",
-       " \n",
-       "          [[-0.0965,  0.0566,  0.1255],\n",
-       "           [-0.0585, -0.0967,  0.1040],\n",
-       "           [ 0.0780, -0.0932, -0.0765]],\n",
-       " \n",
-       "          [[-0.1430, -0.0769,  0.0878],\n",
-       "           [ 0.0445, -0.1451, -0.0879],\n",
-       "           [-0.1104,  0.1238,  0.0362]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[ 0.1287, -0.0361,  0.1400],\n",
-       "           [-0.1236,  0.0723,  0.1403],\n",
-       "           [ 0.0391, -0.0094, -0.0204]],\n",
-       " \n",
-       "          [[-0.0064, -0.0953, -0.1485],\n",
-       "           [-0.1502, -0.0362, -0.0603],\n",
-       "           [-0.1391, -0.0045, -0.1973]],\n",
-       " \n",
-       "          [[ 0.0521,  0.0325, -0.1143],\n",
-       "           [-0.0049,  0.0364, -0.1002],\n",
-       "           [ 0.0636,  0.0741,  0.1504]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.0168, -0.0975, -0.0578],\n",
-       "           [ 0.0566, -0.0591, -0.1632],\n",
-       "           [-0.1642, -0.1372, -0.1504]],\n",
-       " \n",
-       "          [[ 0.1014,  0.0055, -0.0155],\n",
-       "           [-0.1030, -0.0598,  0.0170],\n",
-       "           [-0.0679, -0.1075, -0.1454]],\n",
-       " \n",
-       "          [[ 0.0160,  0.1275,  0.1150],\n",
-       "           [ 0.0614, -0.0059, -0.0255],\n",
-       "           [-0.0216, -0.0436, -0.1342]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0598,  0.0351, -0.0185],\n",
-       "           [ 0.0181,  0.0083,  0.1182],\n",
-       "           [-0.0338, -0.0625,  0.1338]],\n",
-       " \n",
-       "          [[ 0.1275, -0.0143,  0.0083],\n",
-       "           [-0.0107,  0.0066, -0.0726],\n",
-       "           [ 0.1300,  0.0755,  0.0337]],\n",
-       " \n",
-       "          [[-0.0245,  0.1191, -0.0910],\n",
-       "           [-0.0947,  0.0852, -0.0157],\n",
-       "           [-0.1377,  0.0614, -0.0535]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.1022,  0.1393, -0.1138],\n",
-       "           [-0.0811, -0.0876,  0.1376],\n",
-       "           [ 0.1053,  0.1555,  0.0075]],\n",
-       " \n",
-       "          [[ 0.0413,  0.0036, -0.0525],\n",
-       "           [ 0.0228, -0.0040, -0.0177],\n",
-       "           [-0.0717,  0.1396,  0.0573]],\n",
-       " \n",
-       "          [[ 0.0479, -0.0600, -0.1314],\n",
-       "           [-0.0402, -0.0398, -0.0175],\n",
-       "           [-0.0866,  0.0617, -0.0903]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.0249,  0.0288, -0.1226],\n",
-       "           [-0.0341,  0.0274, -0.1171],\n",
-       "           [-0.0768,  0.0409, -0.1136]],\n",
-       " \n",
-       "          [[-0.0526, -0.1906, -0.2001],\n",
-       "           [-0.1391, -0.1448, -0.1320],\n",
-       "           [ 0.0767, -0.1108, -0.0750]],\n",
-       " \n",
-       "          [[ 0.0058,  0.1100,  0.0292],\n",
-       "           [ 0.0885,  0.1144,  0.1095],\n",
-       "           [ 0.0222, -0.0294, -0.1202]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0560, -0.0433, -0.0921],\n",
-       "           [-0.0170, -0.1418,  0.0053],\n",
-       "           [-0.1020, -0.0154, -0.1217]],\n",
-       " \n",
-       "          [[-0.0839,  0.0440, -0.1742],\n",
-       "           [-0.0224, -0.0166,  0.0289],\n",
-       "           [ 0.0053, -0.1238, -0.1059]],\n",
-       " \n",
-       "          [[ 0.0624,  0.1160, -0.1296],\n",
-       "           [-0.1341, -0.0584,  0.0876],\n",
-       "           [ 0.0203,  0.1212, -0.0466]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([1.0076, 1.0280, 1.0149, 0.9544, 1.0110, 1.0128, 1.0226, 1.0228, 1.0165,\n",
-       "         0.9788, 1.0061, 0.9924, 1.0052, 0.9862, 0.9975, 0.9780],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([ 0.0172,  0.0329, -0.0023, -0.0501,  0.0192,  0.0154,  0.0374,  0.0249,\n",
-       "         -0.0227, -0.0521,  0.0212,  0.0081,  0.0123,  0.0424, -0.0368,  0.0434],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[ 0.1616, -0.1505, -0.1635],\n",
-       "           [-0.0272, -0.0679,  0.0628],\n",
-       "           [-0.1153, -0.1936, -0.0313]],\n",
-       " \n",
-       "          [[ 0.2111, -0.1079,  0.1726],\n",
-       "           [ 0.1044, -0.1017, -0.0180],\n",
-       "           [-0.0666,  0.1102, -0.0100]],\n",
-       " \n",
-       "          [[-0.1123,  0.1346,  0.1405],\n",
-       "           [ 0.1449, -0.0846,  0.0985],\n",
-       "           [ 0.0398,  0.1363, -0.0586]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.1717, -0.2065,  0.0306],\n",
-       "           [-0.1139,  0.1230,  0.1928],\n",
-       "           [ 0.1655, -0.0624, -0.2040]],\n",
-       " \n",
-       "          [[ 0.0446,  0.0458, -0.1566],\n",
-       "           [ 0.0371, -0.0792,  0.1368],\n",
-       "           [-0.1283, -0.0216, -0.0518]],\n",
-       " \n",
-       "          [[-0.0557, -0.0888, -0.0709],\n",
-       "           [ 0.1649, -0.0725, -0.1151],\n",
-       "           [-0.0308,  0.0384, -0.1699]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0790,  0.1057, -0.0042],\n",
-       "           [ 0.0827,  0.1589, -0.1911],\n",
-       "           [ 0.1604, -0.1527,  0.0517]],\n",
-       " \n",
-       "          [[-0.1280,  0.1407, -0.1161],\n",
-       "           [ 0.2045, -0.1210,  0.0882],\n",
-       "           [-0.1334,  0.1494,  0.1784]],\n",
-       " \n",
-       "          [[ 0.0480,  0.0328,  0.0950],\n",
-       "           [-0.1571, -0.1619, -0.1349],\n",
-       "           [ 0.1642,  0.0523,  0.2241]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[ 0.1711,  0.2045,  0.1384],\n",
-       "           [ 0.1614,  0.1753,  0.2066],\n",
-       "           [ 0.2008, -0.0172, -0.0548]],\n",
-       " \n",
-       "          [[-0.0841,  0.1302,  0.0624],\n",
-       "           [-0.1635, -0.0412,  0.1688],\n",
-       "           [ 0.1939, -0.2011,  0.1202]],\n",
-       " \n",
-       "          [[ 0.2236, -0.1165, -0.1113],\n",
-       "           [ 0.2108, -0.0788,  0.1730],\n",
-       "           [ 0.0634,  0.1245, -0.0763]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.0529,  0.0246, -0.0982],\n",
-       "           [ 0.1641,  0.0107, -0.1389],\n",
-       "           [-0.0642, -0.2015, -0.2184]],\n",
-       " \n",
-       "          [[ 0.0598,  0.1358,  0.1186],\n",
-       "           [ 0.1717, -0.0579, -0.0436],\n",
-       "           [ 0.0743,  0.1838,  0.1574]],\n",
-       " \n",
-       "          [[-0.0816, -0.1221,  0.1255],\n",
-       "           [ 0.1528,  0.2128,  0.1370],\n",
-       "           [-0.1441,  0.1847,  0.1288]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0912, -0.0964,  0.2250],\n",
-       "           [ 0.0384, -0.1029,  0.0832],\n",
-       "           [-0.1048, -0.0276, -0.1491]],\n",
-       " \n",
-       "          [[ 0.0641,  0.0184, -0.0992],\n",
-       "           [-0.0015,  0.0136,  0.0882],\n",
-       "           [ 0.0863,  0.1465, -0.0262]],\n",
-       " \n",
-       "          [[-0.0839,  0.0566,  0.0126],\n",
-       "           [-0.1098,  0.0829, -0.1367],\n",
-       "           [-0.0517, -0.0986,  0.0833]]],\n",
-       " \n",
-       " \n",
-       "         ...,\n",
-       " \n",
-       " \n",
-       "         [[[-0.0705,  0.1214,  0.0600],\n",
-       "           [-0.1867, -0.0574, -0.0683],\n",
-       "           [-0.0824, -0.1669,  0.0334]],\n",
-       " \n",
-       "          [[ 0.1862, -0.1636,  0.0597],\n",
-       "           [ 0.0281,  0.1775, -0.1095],\n",
-       "           [ 0.1701,  0.0588, -0.1054]],\n",
-       " \n",
-       "          [[ 0.1221,  0.0806,  0.2081],\n",
-       "           [-0.1050, -0.0366,  0.1178],\n",
-       "           [ 0.1781,  0.1811,  0.0574]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.1624, -0.1027,  0.0033],\n",
-       "           [-0.1182, -0.1642, -0.0928],\n",
-       "           [-0.0357,  0.1928,  0.0331]],\n",
-       " \n",
-       "          [[ 0.0544,  0.0595,  0.0916],\n",
-       "           [ 0.0197, -0.0068,  0.0578],\n",
-       "           [ 0.2225, -0.0966, -0.0062]],\n",
-       " \n",
-       "          [[ 0.0620, -0.0276, -0.0780],\n",
-       "           [ 0.0550,  0.1333, -0.0735],\n",
-       "           [ 0.1094, -0.1419, -0.0459]]],\n",
-       " \n",
-       " \n",
-       "         [[[-0.1034, -0.2061, -0.2433],\n",
-       "           [-0.0085, -0.2323, -0.0952],\n",
-       "           [-0.1697, -0.0652, -0.1828]],\n",
-       " \n",
-       "          [[ 0.1703, -0.1481,  0.1117],\n",
-       "           [ 0.0075,  0.1872, -0.0576],\n",
-       "           [-0.1095, -0.0168, -0.1600]],\n",
-       " \n",
-       "          [[ 0.1841, -0.0605, -0.0752],\n",
-       "           [ 0.1067,  0.1777,  0.1592],\n",
-       "           [-0.1228, -0.0443, -0.0912]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.0169,  0.2287,  0.1755],\n",
-       "           [ 0.2482,  0.2199,  0.0631],\n",
-       "           [ 0.0115, -0.1045,  0.1257]],\n",
-       " \n",
-       "          [[ 0.1022, -0.1195, -0.1832],\n",
-       "           [-0.1619,  0.1540, -0.0487],\n",
-       "           [-0.1364,  0.0859,  0.1722]],\n",
-       " \n",
-       "          [[-0.0405, -0.0376, -0.0808],\n",
-       "           [ 0.2144,  0.1141,  0.0493],\n",
-       "           [-0.1107, -0.0719, -0.0082]]],\n",
-       " \n",
-       " \n",
-       "         [[[ 0.2181,  0.0417, -0.1656],\n",
-       "           [-0.0731, -0.0321,  0.2038],\n",
-       "           [ 0.2132, -0.1380,  0.1191]],\n",
-       " \n",
-       "          [[ 0.1539, -0.1055,  0.1101],\n",
-       "           [-0.0683, -0.0139, -0.0901],\n",
-       "           [-0.1190, -0.0711, -0.0488]],\n",
-       " \n",
-       "          [[ 0.1015, -0.0380, -0.1557],\n",
-       "           [-0.0902,  0.0084, -0.1030],\n",
-       "           [-0.0255,  0.0223,  0.1069]],\n",
-       " \n",
-       "          ...,\n",
-       " \n",
-       "          [[-0.1388,  0.0436, -0.0300],\n",
-       "           [-0.0049,  0.1757,  0.0852],\n",
-       "           [-0.0088, -0.0319,  0.2154]],\n",
-       " \n",
-       "          [[-0.0553,  0.1399,  0.1878],\n",
-       "           [-0.1159, -0.0133,  0.1191],\n",
-       "           [ 0.0797, -0.0968, -0.1632]],\n",
-       " \n",
-       "          [[-0.1187,  0.1015,  0.0910],\n",
-       "           [-0.0162,  0.1418, -0.0451],\n",
-       "           [-0.1271,  0.1781, -0.1308]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.9910, 1.1554, 1.0077, 1.2380, 1.1291, 1.0023, 1.1585, 1.1423, 1.1728,\n",
-       "         0.9970, 1.2371, 1.1676, 0.9925, 1.0056, 1.1683, 1.0966],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([-0.0281,  0.1508, -0.0283,  0.1823,  0.1159, -0.0310,  0.0980,  0.1245,\n",
-       "          0.1648, -0.0273,  0.2154,  0.1221, -0.0287, -0.0257,  0.1627,  0.0795],\n",
-       "        device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([[[[-0.0129, -0.1440, -0.0649],\n",
-       "           [ 0.1118,  0.1965, -0.0572],\n",
-       "           [-0.0970, -0.1691,  0.0427]],\n",
-       " \n",
-       "          [[ 0.0937, -0.0752,  0.2470],\n",
-       "           [ 0.0956,  0.2375, -0.0946],\n",
-       "           [ 0.2250, -0.0192,  0.1301]],\n",
-       " \n",
-       "          [[-0.1200, -0.0805, -0.0523],\n",
-       "           [ 0.0423,  0.1265, -0.1830],\n",
-       "           [-0.1974,  0.0801, -0.1671]],\n",
-       " \n",
-       "          [[ 0.2433,  0.1232,  0.3002],\n",
-       "           [-0.0501,  0.0675,  0.2504],\n",
-       "           [ 0.0856,  0.0275, -0.0187]],\n",
-       " \n",
-       "          [[ 0.0224,  0.1952,  0.0036],\n",
-       "           [-0.0290,  0.1149,  0.1318],\n",
-       "           [ 0.2366,  0.1924,  0.0745]],\n",
-       " \n",
-       "          [[ 0.0775,  0.0205, -0.1777],\n",
-       "           [-0.1989, -0.1263, -0.1669],\n",
-       "           [-0.0795,  0.0396,  0.1500]],\n",
-       " \n",
-       "          [[ 0.1321,  0.0938,  0.0280],\n",
-       "           [ 0.2295, -0.0902,  0.2365],\n",
-       "           [ 0.0870, -0.0796,  0.1002]],\n",
-       " \n",
-       "          [[ 0.0151,  0.0656,  0.0490],\n",
-       "           [-0.0364,  0.1108,  0.2847],\n",
-       "           [ 0.1681,  0.2025,  0.0465]],\n",
-       " \n",
-       "          [[ 0.0963,  0.1609,  0.1390],\n",
-       "           [ 0.0539,  0.0377,  0.1020],\n",
-       "           [-0.0404,  0.0753,  0.2785]],\n",
-       " \n",
-       "          [[ 0.0071, -0.0844, -0.1240],\n",
-       "           [-0.0775, -0.1569, -0.0427],\n",
-       "           [-0.0673,  0.1643, -0.1062]],\n",
-       " \n",
-       "          [[ 0.2766,  0.0523,  0.3113],\n",
-       "           [-0.0539,  0.1482, -0.0328],\n",
-       "           [ 0.1575,  0.0821,  0.2410]],\n",
-       " \n",
-       "          [[ 0.1351, -0.0080, -0.0043],\n",
-       "           [ 0.2045,  0.0529,  0.1753],\n",
-       "           [ 0.2473, -0.0622,  0.1165]],\n",
-       " \n",
-       "          [[-0.1707,  0.0332, -0.1627],\n",
-       "           [ 0.1959,  0.0519, -0.0952],\n",
-       "           [-0.1639,  0.1284,  0.0210]],\n",
-       " \n",
-       "          [[-0.1034,  0.0796, -0.1601],\n",
-       "           [ 0.0341, -0.0389, -0.0325],\n",
-       "           [-0.1560, -0.1911, -0.0884]],\n",
-       " \n",
-       "          [[ 0.0573, -0.0777,  0.1729],\n",
-       "           [-0.0101,  0.1146,  0.1940],\n",
-       "           [ 0.0849,  0.1933,  0.1629]],\n",
-       " \n",
-       "          [[ 0.2515,  0.2274,  0.2630],\n",
-       "           [ 0.0925,  0.1598,  0.0565],\n",
-       "           [ 0.2617, -0.0111,  0.2237]]]], device='cuda:0', requires_grad=True),\n",
-       " Parameter containing:\n",
-       " tensor([0.0737], device='cuda:0', requires_grad=True)]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# check parameters\n",
-    "list(model.parameters())"
+    "class testDataset(BaseDataset):\n",
+    "    \"\"\"CamVid Dataset. Read images, apply augmentation and preprocessing transformations.\n",
+    "\n",
+    "    Args:\n",
+    "        images_dir (str): path to images folder\n",
+    "        masks_dir (str): path to segmentation masks folder\n",
+    "        class_values (list): values of classes to extract from segmentation mask\n",
+    "        augmentation (albumentations.Compose): data transfromation pipeline \n",
+    "            (e.g. flip, scale, etc.)\n",
+    "        preprocessing (albumentations.Compose): data preprocessing \n",
+    "            (e.g. noralization, shape manipulation, etc.)\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    CLASSES=[ 'sugarcane', 'weed']\n",
+    "\n",
+    "    def __init__(\n",
+    "            self, \n",
+    "            images_dir, \n",
+    "            masks_dir, \n",
+    "            classes=None, \n",
+    "            augmentation=None, \n",
+    "            preprocessing=None,\n",
+    "    ):\n",
+    "        self.ids = os.listdir(images_dir)\n",
+    "        self.images_fps = [os.path.join(images_dir, image_id) for image_id in self.ids]\n",
+    "        self.masks_fps = [os.path.join(masks_dir, image_id) for image_id in self.ids]\n",
+    "\n",
+    "        # convert str names to class values on masks\n",
+    "        self.class_values = [self.CLASSES.index(cls.lower()) for cls in classes]\n",
+    "\n",
+    "        self.augmentation = augmentation\n",
+    "        self.preprocessing = preprocessing\n",
+    "\n",
+    "    def __getitem__(self, i):\n",
+    "\n",
+    "        # read data\n",
+    "        image = cv2.imread(self.images_fps[i])\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
+    "        mask = cv2.imread(self.masks_fps[i], 0)\n",
+    "\n",
+    "\n",
+    "        # apply augmentations\n",
+    "        if self.augmentation:\n",
+    "            sample = self.augmentation(image=image, mask=mask)\n",
+    "            image, mask = sample['image'], sample['mask']\n",
+    "\n",
+    "        # apply preprocessing\n",
+    "        if self.preprocessing:\n",
+    "            sample = self.preprocessing(image=image, mask=mask)\n",
+    "            image, mask = sample['image'], sample['mask']\n",
+    "\n",
+    "        #t = T.Compose([T.ToTensor()])\n",
+    "        #image = t(image)\n",
+    "        mask = torch.from_numpy(mask).long()\n",
+    "\n",
+    "        return image, mask\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.ids)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#学習曲線\n"
+    "test_dataset = testDataset(\n",
+    "    os.path.join('./val/' 'images'),\n",
+    "    os.path.join('./val/', 'masks'),\n",
+    "    # augmentation=get_training_augmentation(),\n",
+    "    # preprocessing=get_preproessing(preprocessing_fn),\n",
+    "    classes=CLASSES\n",
+    ")\n",
+    "\n",
+    "test_dataloader = DataLoader(test_dataset)"
    ]
   },
   {
@@ -10731,24 +610,294 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import cv2\n",
-    "import PIL\n",
-    "import torch\n",
-    "import matplotlib as plot\n",
-    "import segmentation_models_pytorch as smp\n",
-    "import albumentations\n",
-    "import glob\n",
-    "import tqdm\n",
+    "val_files = glob.glob('./val/images/*.png')\n",
+    "f = val_files[9]\n",
     "\n",
-    "print(cv2.__version__)\n",
-    "print(PIL.__version__)\n",
-    "print(torch.__version__)\n",
-    "print(plot.__version__)\n",
-    "print(smp.__version__)\n",
-    "print(albumentations.__version__)\n",
-    "print(glob: 8080)\n",
-    "print(tqdm.__version__)\n"
+    "palette_image = Image.open(glob.glob('./val/masks/*.png')[9])\n",
+    "PALETTE = palette_image.getpalette()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_model = torch.load(\"./model/DeepLabV3_resnet34.pth\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pixel_accuracy(output, mask):\n",
+    "    with torch.no_grad():\n",
+    "        output = torch.argmax(F.softmax(output, dim=1), dim=1)\n",
+    "        correct = torch.eq(output, mask).int()\n",
+    "        accuracy = float(correct.sum()) / float(correct.numel())\n",
+    "    return accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mIoU(pred_mask, mask, smooth=1e-10, n_classes=len(CLASSES)):\n",
+    "    with torch.no_grad():\n",
+    "        pred_mask = F.softmax(pred_mask, dim=1)\n",
+    "        pred_mask = torch.argmax(pred_mask, dim=1)\n",
+    "        pred_mask = pred_mask.contiguous().view(-1)\n",
+    "        mask = mask.contiguous().view(-1)\n",
+    "\n",
+    "        iou_per_class = []\n",
+    "        for clas in range(0, n_classes):\n",
+    "            true_class = pred_mask == clas\n",
+    "            true_label = mask == clas\n",
+    "\n",
+    "            if true_label.long().sum().item() == 0:\n",
+    "                iou_per_class.append(np.nan)\n",
+    "            else:\n",
+    "                intersect = torch.logical_and(true_class, true_label).sum().float().item()\n",
+    "                union = torch.logical_or(true_class, true_label).sum().float().item()\n",
+    "\n",
+    "                iou = (intersect + smooth) / (union + smooth)\n",
+    "                iou_per_class.append(iou)\n",
+    "        \n",
+    "        return np.nanmean(iou_per_class)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict_image_mask_miou(model, image, mask, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):\n",
+    "    model.eval()\n",
+    "    t = T.Compose([T.ToTensor(), T.Normalize(mean, std)])\n",
+    "\n",
+    "    # print(t)\n",
+    "    # print(image.shape)\n",
+    "\n",
+    "    # image = image.transpose(1, 2, 0).astype(\"float32\")\n",
+    "\n",
+    "    # print(image.shape)\n",
+    "\n",
+    "    image = t(image)\n",
+    "    model.to(device); image=image.to(device)\n",
+    "    mask = mask.to(device)\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "\n",
+    "        image = image.unsqueeze(0)\n",
+    "        mask = mask.unsqueeze(0)\n",
+    "\n",
+    "        output = model(image)\n",
+    "        score = mIoU(output, mask)\n",
+    "        masked = torch.argmax(output, dim=1)\n",
+    "        masked = masked.cpu().squeeze(0)\n",
+    "    \n",
+    "    return masked, score\n",
+    "\n",
+    "def predict_image_mask_pixel(model, image, mask, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):\n",
+    "    model.eval()\n",
+    "    t = T.Compose([T.ToTensor(), T.Normalize(mean, std)])\n",
+    "    image = t(image)\n",
+    "    model.to(device)\n",
+    "    image = image.to(device)\n",
+    "    mask = mask.to(device)\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        \n",
+    "        image = image.unsqueeze(0)\n",
+    "        mask = mask.unsqueeze(0)\n",
+    "\n",
+    "        output = model(image)\n",
+    "        acc = pixel_accuracy(output, mask)\n",
+    "        masked = torch.argmax(output, dim=3)\n",
+    "        masked = masked.cpu().squeeze(0)\n",
+    "\n",
+    "    return masked, acc\n",
+    "\n",
+    "def miou_score(model, test_set):\n",
+    "    score_iou = []\n",
+    "    for i in tqdm(range(len(test_set))):\n",
+    "        img, mask = test_set[i]\n",
+    "        pred_mask, score = predict_image_mask_miou(model, img, mask)\n",
+    "        score_iou.append(score)\n",
+    "    return score_iou"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mob_miou = miou_score(best_model, test_dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"test set miou\", np.mean(mob_miou))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pixel_acc_mean(model, test_set):\n",
+    "    accuracy = []\n",
+    "    for i in tqdm(range(len(test_set))):\n",
+    "        img, mask = test_set[i]\n",
+    "        pred_mask, acc = predict_image_mask_pixel(model, img, mask)\n",
+    "        accuracy.append(acc)\n",
+    "    return accuracy\n",
+    "\n",
+    "def pixel_acc(model, image, mask):\n",
+    "    pred_mask, acc = predict_image_mask_pixel(model, image, mask)\n",
+    "    return acc\n",
+    "\n",
+    "mob_acc = pixel_acc_mean(best_model, test_dataset)\n",
+    "print(f\"Test Set Pixel accuracy {np.mean(mob_acc) * 100:.2f} %\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(len(os.listdir('./val/images/'))):\n",
+    "\n",
+    "    # image2, mask2 = test_dataset[i]\n",
+    "    image2, mask2 = test_dataloader.dataset[i]\n",
+    "    pred_mask2, score2 = predict_image_mask_miou(best_model, image2, mask2)\n",
+    "    accuracy = pixel_acc(best_model, image2, mask2)\n",
+    "\n",
+    "    fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(20,10))\n",
+    "    ax1.imshow(image2)\n",
+    "    ax1.set_title('Picture')\n",
+    "\n",
+    "    mask = Image.fromarray(np.uint8(mask2), mode=\"P\")\n",
+    "    mask.putpalette(PALETTE)\n",
+    "\n",
+    "    ax2.imshow(mask)\n",
+    "    ax2.set_title('Ground truth')\n",
+    "    ax2.set_axis_off()\n",
+    "\n",
+    "    pred_mask2 = Image.fromarray(np.uint8(pred_mask2), mode=\"P\")\n",
+    "    pred_mask2.putpalette(PALETTE)\n",
+    "\n",
+    "    ax3.imshow(pred_mask2)\n",
+    "    ax3.set_title(f'UNet-MobileNet | mIoU {score2:.3f} | acc {accuracy * 100 :.2f}%')\n",
+    "    ax3.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_prediction(n):\n",
+    "\n",
+    "    # img, mask = valid_dataset[n]\n",
+    "    img, mask = valid_loader.dataset[n]\n",
+    "\n",
+    "    fig, ax = plt.subplots(1, 3, tight_layout=True)\n",
+    "    \n",
+    "    ax[0].imshow(img.permute(1, 2, 0))\n",
+    "\n",
+    "    # mask = np.argmax(mask, axis=0)\n",
+    "    mask = Image.fromarray(np.uint8(mask), mode=\"P\")\n",
+    "    mask.putpalette(PALETTE)\n",
+    "\n",
+    "    ax[1].imshow(mask)\n",
+    "\n",
+    "    x = torch.tensor(img).unsqueeze(0)\n",
+    "\n",
+    "    print(x.shape)\n",
+    "\n",
+    "    y = best_model(x.to(device))\n",
+    "    y = y[0].cpu().detach().numpy()\n",
+    "    y = np.argmax(y, axis=0)\n",
+    "\n",
+    "    predict_class_img = Image.fromarray(np.uint8(y), mode=\"P\")\n",
+    "    predict_class_img.putpalette(PALETTE)\n",
+    "    ax[2].imshow(predict_class_img)\n",
+    "\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "best_model = torch.load(\"./model/unet_plus_plus_resnet34.pth\")\n",
+    "best_model.eval()\n",
+    "\n",
+    "paths = os.listdir('./val/masks/')\n",
+    "\n",
+    "# 検証データから\"cat\",\"person\"を含む画像を取得\n",
+    "idx_dict = {\"both\":[],\"sugarcane\":[],\"weed\":[]}\n",
+    "\n",
+    "# 該当の対象物があればpathをリストに加える\n",
+    "for i in range(len(paths)):\n",
+    "\n",
+    "    img = np.asarray(Image.open(f\"./val/masks/{paths[i]}\"))\n",
+    "    unique_class = np.unique(img)\n",
+    "\n",
+    "    if 0 in unique_class and 1 in unique_class:\n",
+    "        idx_dict[\"both\"].append(i)\n",
+    "        \n",
+    "    elif 0 in unique_class:\n",
+    "        idx_dict[\"sugarcane\"].append(i)\n",
+    "        \n",
+    "    elif 1 in unique_class:\n",
+    "        idx_dict[\"weed\"].append(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ラベル毎に実行して結果を確認\n",
+    "for label, idx_list in idx_dict.items():\n",
+    "    print(\"=\"*30 , label, \"=\"*30)\n",
+    "    for i, idx in enumerate(idx_list):\n",
+    "        check_prediction(idx)\n",
+    "        if i==2:\n",
+    "            break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {