diff --git a/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb b/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb
index 56ad82bf..cda42589 100644
--- a/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb
+++ b/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.ipynb
@@ -2,77 +2,33 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "dc826984",
+   "id": "7f44f602",
    "metadata": {},
    "source": [
     "# Vector Quantized Variational Autoencoders and Transformers with MedNIST Dataset\n",
     "\n",
-    "This tutorial illustrates how to use MONAI for training a Vector Quantized Variational Autoencoder (VQVAE)[1] and a transformer model on 2D images.\n",
+    "This tutorial illustrates how to use MONAI for training a Vector Quantized Variational Autoencoder (VQVAE)[1,2] and a transformer model on 2D images.\n",
     "\n",
     "This is a two step process:\n",
     "- We will train our VQVAE model to be able to reconstruct the input images.\n",
     "- This will be followed by using the trained VQVAE model to encode images to feed into the transformer network to train.\n",
     "\n",
-    "We will work with the MedNIST dataset available on MONAI\n",
-    "(https://docs.monai.io/en/stable/apps.html#monai.apps.MedNISTDataset). In order to train faster, we will select just one of the available classes (\"HeadCT\"), resulting in a training set with 7999 2D images.\n",
+    "We will work with the [MedNIST dataset](https://docs.monai.io/en/stable/apps.html#monai.apps.MedNISTDataset) available on MONAI. In order to train faster, we will select just one of the available classes (\"HeadCT\"), resulting in a training set with 7999 2D images.\n",
     "\n",
     "[1] - [Oord et al. \"Neural Discrete Representation Learning\"](https://arxiv.org/abs/1711.00937)\n",
     "\n",
+    "[2] - [Tudosiu et al. \"Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain\"](https://arxiv.org/abs/2209.03177)\n",
+    "\n",
     "\n",
     "### Setup imports"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "5e2e2865",
+   "execution_count": null,
+   "id": "7e829cba",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-03-12 17:59:55,481 - A matching Triton is not available, some optimizations will not be enabled.\n",
-      "Error caught was: No module named 'triton'\n",
-      "MONAI version: 1.2.dev2304\n",
-      "Numpy version: 1.23.5\n",
-      "Pytorch version: 1.13.1+cu117\n",
-      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
-      "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
-      "MONAI __file__: /media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/site-packages/monai/__init__.py\n",
-      "\n",
-      "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.4.10\n",
-      "ITK version: 5.3.0\n",
-      "Nibabel version: 4.0.2\n",
-      "scikit-image version: 0.19.3\n",
-      "Pillow version: 9.3.0\n",
-      "Tensorboard version: 2.11.0\n",
-      "gdown version: 4.6.0\n",
-      "TorchVision version: 0.14.1+cu117\n",
-      "tqdm version: 4.64.1\n",
-      "lmdb version: 1.4.0\n",
-      "psutil version: 5.9.4\n",
-      "pandas version: 1.5.3\n",
-      "einops version: 0.6.0\n",
-      "transformers version: 4.21.3\n",
-      "mlflow version: 2.1.1\n",
-      "pynrrd version: 1.0.0\n",
-      "\n",
-      "For details about installing the optional dependencies, please visit:\n",
-      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Copyright 2020 MONAI Consortium\n",
     "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
@@ -112,8 +68,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "5eecf5fa",
+   "execution_count": null,
+   "id": "e11e1e9c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,7 +79,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eeeb2157",
+   "id": "4f71d660",
    "metadata": {},
    "source": [
     "### Setup a data directory and download dataset\n",
@@ -134,18 +90,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "44d781fc",
+   "execution_count": null,
+   "id": "8a303c95",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/tmp/tmpkrig75nx\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
     "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
@@ -154,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7b331a2",
+   "id": "c6975501",
    "metadata": {},
    "source": [
     "### Download training data"
@@ -162,47 +110,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "d89063f8",
+   "execution_count": null,
+   "id": "de2fd1d5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "MedNIST.tar.gz: 59.0MB [00:04, 13.2MB/s]                                                                                                                                                                                       "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-03-12 18:00:00,240 - INFO - Downloaded: /tmp/tmpkrig75nx/MedNIST.tar.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-03-12 18:00:00,338 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-03-12 18:00:00,338 - INFO - Writing into directory: /tmp/tmpkrig75nx.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:14<00:00, 3286.26it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_data = MedNISTDataset(root_dir=root_dir, section=\"training\", download=True, seed=0)\n",
     "train_datalist = [{\"image\": item[\"image\"]} for item in train_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
@@ -224,12 +135,12 @@
     "    ]\n",
     ")\n",
     "train_ds = Dataset(data=train_datalist, transform=train_transforms)\n",
-    "train_loader = DataLoader(train_ds, batch_size=128, shuffle=True, num_workers=4)"
+    "train_loader = DataLoader(train_ds, batch_size=128, shuffle=True, num_workers=4, persistent_workers=True)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "19ce954e",
+   "id": "9eb87583",
    "metadata": {},
    "source": [
     "### Visualse some examples from the dataset"
@@ -237,21 +148,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "510f986a",
+   "execution_count": null,
+   "id": "7b057d0e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot 3 examples from the training set\n",
     "check_data = first(train_loader)\n",
@@ -263,7 +163,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "acda5546",
+   "id": "a9f6b281",
    "metadata": {},
    "source": [
     "### Download Validation Data"
@@ -271,27 +171,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "cde9bca8",
+   "execution_count": null,
+   "id": "ee074b4c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2023-03-12 18:00:19,594 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-03-12 18:00:19,594 - INFO - File exists: /tmp/tmpkrig75nx/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-03-12 18:00:19,594 - INFO - Non-empty folder exists in /tmp/tmpkrig75nx/MedNIST, skipped extracting.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3425.13it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "val_data = MedNISTDataset(root_dir=root_dir, section=\"validation\", download=True, seed=0)\n",
     "val_datalist = [{\"image\": item[\"image\"]} for item in val_data.data if item[\"class_name\"] == \"HeadCT\"]\n",
@@ -303,12 +186,12 @@
     "    ]\n",
     ")\n",
     "val_ds = Dataset(data=val_datalist, transform=val_transforms)\n",
-    "val_loader = DataLoader(val_ds, batch_size=128, shuffle=True, num_workers=4)"
+    "val_loader = DataLoader(val_ds, batch_size=128, shuffle=True, num_workers=4, persistent_workers=True)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ba5fac10",
+   "id": "a2a79663",
    "metadata": {},
    "source": [
     "## VQVAE Training\n",
@@ -317,7 +200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cec13fba",
+   "id": "a27e12e1",
    "metadata": {},
    "source": [
     "### Define network, optimizer and losses"
@@ -325,167 +208,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "8bc82d96",
+   "execution_count": null,
+   "id": "26e574b1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using cuda\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "VQVAE(\n",
-       "  (encoder): Encoder(\n",
-       "    (blocks): ModuleList(\n",
-       "      (0): Convolution(\n",
-       "        (conv): Conv2d(1, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "        (adn): ADN(\n",
-       "          (A): ReLU()\n",
-       "        )\n",
-       "      )\n",
-       "      (1): VQVAEResidualUnit(\n",
-       "        (conv1): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          (adn): ADN(\n",
-       "            (D): Dropout(p=0.0, inplace=False)\n",
-       "            (A): ReLU()\n",
-       "          )\n",
-       "        )\n",
-       "        (conv2): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "      (2): VQVAEResidualUnit(\n",
-       "        (conv1): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          (adn): ADN(\n",
-       "            (D): Dropout(p=0.0, inplace=False)\n",
-       "            (A): ReLU()\n",
-       "          )\n",
-       "        )\n",
-       "        (conv2): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "      (3): Convolution(\n",
-       "        (conv): Conv2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "        (adn): ADN(\n",
-       "          (D): Dropout(p=0.0, inplace=False)\n",
-       "          (A): ReLU()\n",
-       "        )\n",
-       "      )\n",
-       "      (4): VQVAEResidualUnit(\n",
-       "        (conv1): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          (adn): ADN(\n",
-       "            (D): Dropout(p=0.0, inplace=False)\n",
-       "            (A): ReLU()\n",
-       "          )\n",
-       "        )\n",
-       "        (conv2): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "      (5): VQVAEResidualUnit(\n",
-       "        (conv1): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          (adn): ADN(\n",
-       "            (D): Dropout(p=0.0, inplace=False)\n",
-       "            (A): ReLU()\n",
-       "          )\n",
-       "        )\n",
-       "        (conv2): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "      (6): Convolution(\n",
-       "        (conv): Conv2d(256, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (decoder): Decoder(\n",
-       "    (blocks): ModuleList(\n",
-       "      (0): Convolution(\n",
-       "        (conv): Conv2d(32, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "      )\n",
-       "      (1): VQVAEResidualUnit(\n",
-       "        (conv1): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          (adn): ADN(\n",
-       "            (D): Dropout(p=0.0, inplace=False)\n",
-       "            (A): ReLU()\n",
-       "          )\n",
-       "        )\n",
-       "        (conv2): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "      (2): VQVAEResidualUnit(\n",
-       "        (conv1): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          (adn): ADN(\n",
-       "            (D): Dropout(p=0.0, inplace=False)\n",
-       "            (A): ReLU()\n",
-       "          )\n",
-       "        )\n",
-       "        (conv2): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "      (3): Convolution(\n",
-       "        (conv): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "        (adn): ADN(\n",
-       "          (D): Dropout(p=0.0, inplace=False)\n",
-       "          (A): ReLU()\n",
-       "        )\n",
-       "      )\n",
-       "      (4): VQVAEResidualUnit(\n",
-       "        (conv1): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          (adn): ADN(\n",
-       "            (D): Dropout(p=0.0, inplace=False)\n",
-       "            (A): ReLU()\n",
-       "          )\n",
-       "        )\n",
-       "        (conv2): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "      (5): VQVAEResidualUnit(\n",
-       "        (conv1): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "          (adn): ADN(\n",
-       "            (D): Dropout(p=0.0, inplace=False)\n",
-       "            (A): ReLU()\n",
-       "          )\n",
-       "        )\n",
-       "        (conv2): Convolution(\n",
-       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
-       "        )\n",
-       "      )\n",
-       "      (6): Convolution(\n",
-       "        (conv): ConvTranspose2d(256, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
-       "      )\n",
-       "    )\n",
-       "  )\n",
-       "  (quantizer): VectorQuantizer(\n",
-       "    (quantizer): EMAQuantizer(\n",
-       "      (embedding): Embedding(256, 32)\n",
-       "    )\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
     "print(f\"Using {device}\")\n",
@@ -501,13 +227,13 @@
     "    num_embeddings=256,\n",
     "    embedding_dim=32,\n",
     ")\n",
-    "vqvae_model.to(device)"
+    "vqvae_model = vqvae_model.to(device)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "675d2618",
+   "execution_count": null,
+   "id": "fe69c9bb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -517,7 +243,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "19ad3fd0",
+   "id": "525823b0",
    "metadata": {},
    "source": [
     "### VQVAE Model training\n",
@@ -526,124 +252,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "42a56f13",
+   "execution_count": null,
+   "id": "f2200303",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|█████████████████| 63/63 [00:30<00:00,  2.07it/s, recons_loss=0.0985, quantization_loss=1.76e-5]\n",
-      "Epoch 1: 100%|█████████████████| 63/63 [00:30<00:00,  2.09it/s, recons_loss=0.0478, quantization_loss=2.04e-5]\n",
-      "Epoch 2: 100%|█████████████████| 63/63 [00:30<00:00,  2.06it/s, recons_loss=0.0377, quantization_loss=1.79e-5]\n",
-      "Epoch 3: 100%|█████████████████| 63/63 [00:30<00:00,  2.05it/s, recons_loss=0.0333, quantization_loss=1.88e-5]\n",
-      "Epoch 4: 100%|█████████████████| 63/63 [00:30<00:00,  2.04it/s, recons_loss=0.0307, quantization_loss=1.77e-5]\n",
-      "Epoch 5: 100%|█████████████████| 63/63 [00:31<00:00,  2.03it/s, recons_loss=0.0286, quantization_loss=1.55e-5]\n",
-      "Epoch 6: 100%|██████████████████| 63/63 [00:31<00:00,  2.02it/s, recons_loss=0.0279, quantization_loss=1.6e-5]\n",
-      "Epoch 7: 100%|█████████████████| 63/63 [00:31<00:00,  1.99it/s, recons_loss=0.0265, quantization_loss=1.42e-5]\n",
-      "Epoch 8: 100%|█████████████████| 63/63 [00:31<00:00,  1.97it/s, recons_loss=0.0259, quantization_loss=1.93e-5]\n",
-      "Epoch 9: 100%|█████████████████| 63/63 [00:32<00:00,  1.95it/s, recons_loss=0.0253, quantization_loss=2.28e-5]\n",
-      "Epoch 10: 100%|████████████████| 63/63 [00:32<00:00,  1.96it/s, recons_loss=0.0247, quantization_loss=1.73e-5]\n",
-      "Epoch 11: 100%|████████████████| 63/63 [00:32<00:00,  1.95it/s, recons_loss=0.0238, quantization_loss=2.45e-5]\n",
-      "Epoch 12: 100%|████████████████| 63/63 [00:32<00:00,  1.95it/s, recons_loss=0.0229, quantization_loss=2.41e-5]\n",
-      "Epoch 13: 100%|████████████████| 63/63 [00:32<00:00,  1.96it/s, recons_loss=0.0227, quantization_loss=3.02e-5]\n",
-      "Epoch 14: 100%|████████████████| 63/63 [00:32<00:00,  1.97it/s, recons_loss=0.0228, quantization_loss=2.44e-5]\n",
-      "Epoch 15: 100%|████████████████| 63/63 [00:32<00:00,  1.96it/s, recons_loss=0.0223, quantization_loss=2.72e-5]\n",
-      "Epoch 16: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0222, quantization_loss=2.97e-5]\n",
-      "Epoch 17: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0222, quantization_loss=2.88e-5]\n",
-      "Epoch 18: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0218, quantization_loss=3.26e-5]\n",
-      "Epoch 19: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0222, quantization_loss=3.42e-5]\n",
-      "Epoch 20: 100%|████████████████| 63/63 [00:31<00:00,  1.99it/s, recons_loss=0.0221, quantization_loss=3.38e-5]\n",
-      "Epoch 21: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0209, quantization_loss=3.94e-5]\n",
-      "Epoch 22: 100%|█████████████████| 63/63 [00:31<00:00,  1.97it/s, recons_loss=0.0206, quantization_loss=3.5e-5]\n",
-      "Epoch 23: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0211, quantization_loss=3.82e-5]\n",
-      "Epoch 24: 100%|████████████████| 63/63 [00:31<00:00,  1.99it/s, recons_loss=0.0212, quantization_loss=3.78e-5]\n",
-      "Epoch 25: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0204, quantization_loss=3.85e-5]\n",
-      "Epoch 26: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0209, quantization_loss=4.28e-5]\n",
-      "Epoch 27: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0205, quantization_loss=4.02e-5]\n",
-      "Epoch 28: 100%|████████████████| 63/63 [00:31<00:00,  1.99it/s, recons_loss=0.0207, quantization_loss=4.27e-5]\n",
-      "Epoch 29: 100%|████████████████| 63/63 [00:31<00:00,  1.97it/s, recons_loss=0.0206, quantization_loss=3.63e-5]\n",
-      "Epoch 30: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0198, quantization_loss=4.78e-5]\n",
-      "Epoch 31: 100%|██████████████████| 63/63 [00:31<00:00,  1.99it/s, recons_loss=0.02, quantization_loss=4.43e-5]\n",
-      "Epoch 32: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0205, quantization_loss=4.15e-5]\n",
-      "Epoch 33: 100%|████████████████| 63/63 [00:31<00:00,  1.99it/s, recons_loss=0.0203, quantization_loss=4.31e-5]\n",
-      "Epoch 34: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0189, quantization_loss=4.61e-5]\n",
-      "Epoch 35: 100%|█████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0186, quantization_loss=5.7e-5]\n",
-      "Epoch 36: 100%|████████████████| 63/63 [00:32<00:00,  1.95it/s, recons_loss=0.0185, quantization_loss=5.69e-5]\n",
-      "Epoch 37: 100%|█████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0185, quantization_loss=5.2e-5]\n",
-      "Epoch 38: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0186, quantization_loss=5.33e-5]\n",
-      "Epoch 39: 100%|█████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0186, quantization_loss=5.5e-5]\n",
-      "Epoch 40: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0186, quantization_loss=5.49e-5]\n",
-      "Epoch 41: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0191, quantization_loss=5.34e-5]\n",
-      "Epoch 42: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0196, quantization_loss=4.56e-5]\n",
-      "Epoch 43: 100%|████████████████| 63/63 [00:32<00:00,  1.96it/s, recons_loss=0.0191, quantization_loss=5.13e-5]\n",
-      "Epoch 44: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0194, quantization_loss=5.07e-5]\n",
-      "Epoch 45: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0196, quantization_loss=4.85e-5]\n",
-      "Epoch 46: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0188, quantization_loss=4.44e-5]\n",
-      "Epoch 47: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0188, quantization_loss=5.63e-5]\n",
-      "Epoch 48: 100%|█████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0181, quantization_loss=5.9e-5]\n",
-      "Epoch 49: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0176, quantization_loss=5.55e-5]\n",
-      "Epoch 50: 100%|█████████████████| 63/63 [00:31<00:00,  1.97it/s, recons_loss=0.0176, quantization_loss=5.3e-5]\n",
-      "Epoch 51: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0176, quantization_loss=5.44e-5]\n",
-      "Epoch 52: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0177, quantization_loss=5.36e-5]\n",
-      "Epoch 53: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0177, quantization_loss=5.14e-5]\n",
-      "Epoch 54: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0181, quantization_loss=5.81e-5]\n",
-      "Epoch 55: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0191, quantization_loss=4.53e-5]\n",
-      "Epoch 56: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0185, quantization_loss=4.75e-5]\n",
-      "Epoch 57: 100%|████████████████| 63/63 [00:32<00:00,  1.96it/s, recons_loss=0.0185, quantization_loss=4.54e-5]\n",
-      "Epoch 58: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0177, quantization_loss=6.18e-5]\n",
-      "Epoch 59: 100%|█████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0173, quantization_loss=6.5e-5]\n",
-      "Epoch 60: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0172, quantization_loss=5.93e-5]\n",
-      "Epoch 61: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0171, quantization_loss=6.64e-5]\n",
-      "Epoch 62: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0173, quantization_loss=6.23e-5]\n",
-      "Epoch 63: 100%|████████████████| 63/63 [00:31<00:00,  1.97it/s, recons_loss=0.0184, quantization_loss=4.68e-5]\n",
-      "Epoch 64: 100%|███████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0189, quantization_loss=5e-5]\n",
-      "Epoch 65: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0179, quantization_loss=6.14e-5]\n",
-      "Epoch 66: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0172, quantization_loss=5.79e-5]\n",
-      "Epoch 67: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0171, quantization_loss=5.44e-5]\n",
-      "Epoch 68: 100%|████████████████| 63/63 [00:31<00:00,  1.97it/s, recons_loss=0.0175, quantization_loss=5.42e-5]\n",
-      "Epoch 69: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0173, quantization_loss=6.11e-5]\n",
-      "Epoch 70: 100%|████████████████| 63/63 [00:31<00:00,  1.97it/s, recons_loss=0.0179, quantization_loss=5.79e-5]\n",
-      "Epoch 71: 100%|█████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0176, quantization_loss=6.1e-5]\n",
-      "Epoch 72: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0175, quantization_loss=5.29e-5]\n",
-      "Epoch 73: 100%|█████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.017, quantization_loss=5.79e-5]\n",
-      "Epoch 74: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0169, quantization_loss=6.35e-5]\n",
-      "Epoch 75: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0165, quantization_loss=5.69e-5]\n",
-      "Epoch 76: 100%|████████████████| 63/63 [00:31<00:00,  1.97it/s, recons_loss=0.0167, quantization_loss=6.24e-5]\n",
-      "Epoch 77: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0166, quantization_loss=6.07e-5]\n",
-      "Epoch 78: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0165, quantization_loss=6.39e-5]\n",
-      "Epoch 79: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0165, quantization_loss=5.46e-5]\n",
-      "Epoch 80: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0167, quantization_loss=6.68e-5]\n",
-      "Epoch 81: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0169, quantization_loss=5.71e-5]\n",
-      "Epoch 82: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0173, quantization_loss=5.63e-5]\n",
-      "Epoch 83: 100%|████████████████| 63/63 [00:31<00:00,  1.97it/s, recons_loss=0.0179, quantization_loss=6.25e-5]\n",
-      "Epoch 84: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0166, quantization_loss=5.68e-5]\n",
-      "Epoch 85: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0163, quantization_loss=5.15e-5]\n",
-      "Epoch 86: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0167, quantization_loss=4.76e-5]\n",
-      "Epoch 87: 100%|█████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0172, quantization_loss=5.6e-5]\n",
-      "Epoch 88: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0166, quantization_loss=5.42e-5]\n",
-      "Epoch 89: 100%|████████████████| 63/63 [00:32<00:00,  1.96it/s, recons_loss=0.0164, quantization_loss=6.15e-5]\n",
-      "Epoch 90: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0163, quantization_loss=6.12e-5]\n",
-      "Epoch 91: 100%|████████████████| 63/63 [00:31<00:00,  1.97it/s, recons_loss=0.0164, quantization_loss=5.88e-5]\n",
-      "Epoch 92: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0163, quantization_loss=5.76e-5]\n",
-      "Epoch 93: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0173, quantization_loss=4.84e-5]\n",
-      "Epoch 94: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0171, quantization_loss=5.66e-5]\n",
-      "Epoch 95: 100%|█████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.017, quantization_loss=5.74e-5]\n",
-      "Epoch 96: 100%|████████████████| 63/63 [00:32<00:00,  1.97it/s, recons_loss=0.0162, quantization_loss=6.01e-5]\n",
-      "Epoch 97: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0164, quantization_loss=6.28e-5]\n",
-      "Epoch 98: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0161, quantization_loss=6.71e-5]\n",
-      "Epoch 99: 100%|████████████████| 63/63 [00:31<00:00,  1.98it/s, recons_loss=0.0161, quantization_loss=6.23e-5]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train completed, total time: 3198.0845563411713.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_epochs = 100\n",
     "val_interval = 10\n",
@@ -712,7 +324,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86d238c9",
+   "id": "565f465e",
    "metadata": {},
    "source": [
     "### VQVE Loss Curve"
@@ -720,21 +332,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "96730fbb",
+   "execution_count": null,
+   "id": "76a83c60",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.style.use(\"ggplot\")\n",
     "plt.title(\"Learning Curves\", fontsize=20)\n",
@@ -756,7 +357,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e61de2f8",
+   "id": "47b3c37e",
    "metadata": {},
    "source": [
     "###  Plotting  evolution of reconstruction performance"
@@ -764,23 +365,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "bccef846",
+   "execution_count": null,
+   "id": "0518fe65",
    "metadata": {
     "lines_to_next_cell": 2
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1800x3000 with 10 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot every evaluation as a new line and example as columns\n",
     "val_samples = np.linspace(val_interval, n_epochs, int(n_epochs / val_interval))\n",
@@ -796,7 +386,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffa58261",
+   "id": "329286c5",
    "metadata": {},
    "source": [
     "### Plot reconstructions of final trained vqvae model"
@@ -804,21 +394,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "d6efa4c9",
+   "execution_count": null,
+   "id": "7c519f43",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(nrows=1, ncols=2)\n",
     "ax[0].imshow(images[0, 0].detach().cpu(), vmin=0, vmax=1, cmap=\"gray\")\n",
@@ -832,7 +411,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b40490ea",
+   "id": "5eec424e",
    "metadata": {},
    "source": [
     "## Transformer Training\n",
@@ -847,7 +426,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca886d3e",
+   "id": "a36e54d0",
    "metadata": {},
    "source": [
     "### Datasets\n",
@@ -856,18 +435,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "9c888aa5",
+   "execution_count": null,
+   "id": "fd99689f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_loader = DataLoader(train_ds, batch_size=16, shuffle=True, num_workers=4)\n",
-    "val_loader = DataLoader(val_ds, batch_size=16, shuffle=True, num_workers=4)"
+    "train_loader = DataLoader(train_ds, batch_size=16, shuffle=True, num_workers=4, persistent_workers=True)\n",
+    "val_loader = DataLoader(val_ds, batch_size=16, shuffle=True, num_workers=4, persistent_workers=True)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c11cccc3",
+   "id": "d2ffb784",
    "metadata": {},
    "source": [
     "### Latent sequence ordering\n",
@@ -876,8 +455,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "d95e1cc1",
+   "execution_count": null,
+   "id": "e74db63d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -886,8 +465,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "8afcb16e",
+   "execution_count": null,
+   "id": "07c9e223",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -906,7 +485,27 @@
   },
   {
    "cell_type": "markdown",
-   "id": "295e1970",
+   "id": "8cd6a92b",
+   "metadata": {},
+   "source": [
+    "### Begin of sentence token (BOS)\n",
+    "\n",
+    "After we transform the data into a 1D representation, we need to define a value token to define the begining of the sequence (a.k.a., BOS token in NLP). This way, when we feed it to the transformer, the transformer will predict what is the value of the first valid token in the sequence. Since in the VQ-VAE, we are using the token values from 0 to 255 to define the elements of the codebook, here we will specify the next value as the be BOS, i.e. 256."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a6096c6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bos_token = 256"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2eae1de",
    "metadata": {},
    "source": [
     "## Define Network, optimizer and losses"
@@ -914,447 +513,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "acaa850a",
+   "execution_count": null,
+   "id": "5e635b70",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DecoderOnlyTransformer(\n",
-       "  (model): TransformerWrapper(\n",
-       "    (token_emb): TokenEmbedding(\n",
-       "      (emb): Embedding(257, 96)\n",
-       "    )\n",
-       "    (pos_emb): AbsolutePositionalEmbedding(\n",
-       "      (emb): Embedding(256, 96)\n",
-       "    )\n",
-       "    (post_emb_norm): Identity()\n",
-       "    (emb_dropout): Dropout(p=0.0, inplace=False)\n",
-       "    (project_emb): Identity()\n",
-       "    (attn_layers): Decoder(\n",
-       "      (layers): ModuleList(\n",
-       "        (0): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): Attention(\n",
-       "            (to_q): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_k): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_v): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (dropout): Dropout(p=0.0, inplace=False)\n",
-       "            (to_out): Linear(in_features=512, out_features=96, bias=False)\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (1): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): FeedForward(\n",
-       "            (ff): Sequential(\n",
-       "              (0): Sequential(\n",
-       "                (0): Linear(in_features=96, out_features=384, bias=True)\n",
-       "                (1): GELU(approximate='none')\n",
-       "              )\n",
-       "              (1): Identity()\n",
-       "              (2): Dropout(p=0.0, inplace=False)\n",
-       "              (3): Linear(in_features=384, out_features=96, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (2): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): Attention(\n",
-       "            (to_q): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_k): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_v): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (dropout): Dropout(p=0.0, inplace=False)\n",
-       "            (to_out): Linear(in_features=512, out_features=96, bias=False)\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (3): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): FeedForward(\n",
-       "            (ff): Sequential(\n",
-       "              (0): Sequential(\n",
-       "                (0): Linear(in_features=96, out_features=384, bias=True)\n",
-       "                (1): GELU(approximate='none')\n",
-       "              )\n",
-       "              (1): Identity()\n",
-       "              (2): Dropout(p=0.0, inplace=False)\n",
-       "              (3): Linear(in_features=384, out_features=96, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (4): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): Attention(\n",
-       "            (to_q): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_k): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_v): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (dropout): Dropout(p=0.0, inplace=False)\n",
-       "            (to_out): Linear(in_features=512, out_features=96, bias=False)\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (5): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): FeedForward(\n",
-       "            (ff): Sequential(\n",
-       "              (0): Sequential(\n",
-       "                (0): Linear(in_features=96, out_features=384, bias=True)\n",
-       "                (1): GELU(approximate='none')\n",
-       "              )\n",
-       "              (1): Identity()\n",
-       "              (2): Dropout(p=0.0, inplace=False)\n",
-       "              (3): Linear(in_features=384, out_features=96, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (6): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): Attention(\n",
-       "            (to_q): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_k): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_v): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (dropout): Dropout(p=0.0, inplace=False)\n",
-       "            (to_out): Linear(in_features=512, out_features=96, bias=False)\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (7): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): FeedForward(\n",
-       "            (ff): Sequential(\n",
-       "              (0): Sequential(\n",
-       "                (0): Linear(in_features=96, out_features=384, bias=True)\n",
-       "                (1): GELU(approximate='none')\n",
-       "              )\n",
-       "              (1): Identity()\n",
-       "              (2): Dropout(p=0.0, inplace=False)\n",
-       "              (3): Linear(in_features=384, out_features=96, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (8): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): Attention(\n",
-       "            (to_q): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_k): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_v): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (dropout): Dropout(p=0.0, inplace=False)\n",
-       "            (to_out): Linear(in_features=512, out_features=96, bias=False)\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (9): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): FeedForward(\n",
-       "            (ff): Sequential(\n",
-       "              (0): Sequential(\n",
-       "                (0): Linear(in_features=96, out_features=384, bias=True)\n",
-       "                (1): GELU(approximate='none')\n",
-       "              )\n",
-       "              (1): Identity()\n",
-       "              (2): Dropout(p=0.0, inplace=False)\n",
-       "              (3): Linear(in_features=384, out_features=96, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (10): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): Attention(\n",
-       "            (to_q): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_k): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_v): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (dropout): Dropout(p=0.0, inplace=False)\n",
-       "            (to_out): Linear(in_features=512, out_features=96, bias=False)\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (11): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): FeedForward(\n",
-       "            (ff): Sequential(\n",
-       "              (0): Sequential(\n",
-       "                (0): Linear(in_features=96, out_features=384, bias=True)\n",
-       "                (1): GELU(approximate='none')\n",
-       "              )\n",
-       "              (1): Identity()\n",
-       "              (2): Dropout(p=0.0, inplace=False)\n",
-       "              (3): Linear(in_features=384, out_features=96, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (12): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): Attention(\n",
-       "            (to_q): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_k): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_v): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (dropout): Dropout(p=0.0, inplace=False)\n",
-       "            (to_out): Linear(in_features=512, out_features=96, bias=False)\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (13): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): FeedForward(\n",
-       "            (ff): Sequential(\n",
-       "              (0): Sequential(\n",
-       "                (0): Linear(in_features=96, out_features=384, bias=True)\n",
-       "                (1): GELU(approximate='none')\n",
-       "              )\n",
-       "              (1): Identity()\n",
-       "              (2): Dropout(p=0.0, inplace=False)\n",
-       "              (3): Linear(in_features=384, out_features=96, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (14): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): Attention(\n",
-       "            (to_q): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_k): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_v): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (dropout): Dropout(p=0.0, inplace=False)\n",
-       "            (to_out): Linear(in_features=512, out_features=96, bias=False)\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (15): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): FeedForward(\n",
-       "            (ff): Sequential(\n",
-       "              (0): Sequential(\n",
-       "                (0): Linear(in_features=96, out_features=384, bias=True)\n",
-       "                (1): GELU(approximate='none')\n",
-       "              )\n",
-       "              (1): Identity()\n",
-       "              (2): Dropout(p=0.0, inplace=False)\n",
-       "              (3): Linear(in_features=384, out_features=96, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (16): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): Attention(\n",
-       "            (to_q): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_k): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_v): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (dropout): Dropout(p=0.0, inplace=False)\n",
-       "            (to_out): Linear(in_features=512, out_features=96, bias=False)\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (17): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): FeedForward(\n",
-       "            (ff): Sequential(\n",
-       "              (0): Sequential(\n",
-       "                (0): Linear(in_features=96, out_features=384, bias=True)\n",
-       "                (1): GELU(approximate='none')\n",
-       "              )\n",
-       "              (1): Identity()\n",
-       "              (2): Dropout(p=0.0, inplace=False)\n",
-       "              (3): Linear(in_features=384, out_features=96, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (18): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): Attention(\n",
-       "            (to_q): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_k): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_v): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (dropout): Dropout(p=0.0, inplace=False)\n",
-       "            (to_out): Linear(in_features=512, out_features=96, bias=False)\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (19): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): FeedForward(\n",
-       "            (ff): Sequential(\n",
-       "              (0): Sequential(\n",
-       "                (0): Linear(in_features=96, out_features=384, bias=True)\n",
-       "                (1): GELU(approximate='none')\n",
-       "              )\n",
-       "              (1): Identity()\n",
-       "              (2): Dropout(p=0.0, inplace=False)\n",
-       "              (3): Linear(in_features=384, out_features=96, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (20): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): Attention(\n",
-       "            (to_q): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_k): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_v): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (dropout): Dropout(p=0.0, inplace=False)\n",
-       "            (to_out): Linear(in_features=512, out_features=96, bias=False)\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (21): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): FeedForward(\n",
-       "            (ff): Sequential(\n",
-       "              (0): Sequential(\n",
-       "                (0): Linear(in_features=96, out_features=384, bias=True)\n",
-       "                (1): GELU(approximate='none')\n",
-       "              )\n",
-       "              (1): Identity()\n",
-       "              (2): Dropout(p=0.0, inplace=False)\n",
-       "              (3): Linear(in_features=384, out_features=96, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (22): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): Attention(\n",
-       "            (to_q): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_k): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (to_v): Linear(in_features=96, out_features=512, bias=False)\n",
-       "            (dropout): Dropout(p=0.0, inplace=False)\n",
-       "            (to_out): Linear(in_features=512, out_features=96, bias=False)\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "        (23): ModuleList(\n",
-       "          (0): ModuleList(\n",
-       "            (0): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "            (1): None\n",
-       "            (2): None\n",
-       "          )\n",
-       "          (1): FeedForward(\n",
-       "            (ff): Sequential(\n",
-       "              (0): Sequential(\n",
-       "                (0): Linear(in_features=96, out_features=384, bias=True)\n",
-       "                (1): GELU(approximate='none')\n",
-       "              )\n",
-       "              (1): Identity()\n",
-       "              (2): Dropout(p=0.0, inplace=False)\n",
-       "              (3): Linear(in_features=384, out_features=96, bias=True)\n",
-       "            )\n",
-       "          )\n",
-       "          (2): Residual()\n",
-       "        )\n",
-       "      )\n",
-       "    )\n",
-       "    (norm): LayerNorm((96,), eps=1e-05, elementwise_affine=True)\n",
-       "    (to_logits): Linear(in_features=96, out_features=257, bias=True)\n",
-       "  )\n",
-       ")"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
     "\n",
@@ -1365,13 +527,13 @@
     "    attn_layers_depth=12,\n",
     "    attn_layers_heads=8,\n",
     ")\n",
-    "transformer_model.to(device)"
+    "transformer_model = transformer_model.to(device)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "c64b1237",
+   "execution_count": null,
+   "id": "66e9ba89",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -1383,7 +545,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad0849c3",
+   "id": "d7e2037a",
    "metadata": {
     "lines_to_next_cell": 2
    },
@@ -1393,13 +555,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "eedfc55e",
-   "metadata": {},
+   "execution_count": null,
+   "id": "b4e89ee2",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "@torch.no_grad()\n",
-    "def generate(net, vqvae_model, starting_tokens, seq_len, **kwargs):\n",
+    "def generate(net, starting_tokens, seq_len, bos_token):\n",
     "    progress_bar = iter(range(seq_len))\n",
     "\n",
     "    latent_seq = starting_tokens.long()\n",
@@ -1419,7 +585,7 @@
     "        # apply softmax to convert logits to (normalized) probabilities\n",
     "        probs = F.softmax(logits, dim=-1)\n",
     "        # remove the chance to be sampled the BOS token\n",
-    "        probs[:, vqvae_model.num_embeddings - 1] = 0\n",
+    "        probs[:, bos_token] = 0\n",
     "\n",
     "        # sample from the distribution\n",
     "        idx_next = torch.multinomial(probs, num_samples=1)\n",
@@ -1432,83 +598,27 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a54894d1",
-   "metadata": {},
+   "id": "32db0efc",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "### Transformer Model Training\n",
-    "We will train the model for 100 epochs"
+    "We will train the model for 50 epochs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "34364372",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0: 100%|████████████████████████████████████████████████| 500/500 [00:43<00:00, 11.58it/s, ce_loss=3.44]\n",
-      "Epoch 1: 100%|████████████████████████████████████████████████| 500/500 [00:43<00:00, 11.54it/s, ce_loss=2.84]\n",
-      "Epoch 2: 100%|████████████████████████████████████████████████| 500/500 [00:43<00:00, 11.52it/s, ce_loss=2.66]\n",
-      "Epoch 3: 100%|████████████████████████████████████████████████| 500/500 [00:44<00:00, 11.35it/s, ce_loss=2.56]\n",
-      "Epoch 4: 100%|████████████████████████████████████████████████| 500/500 [00:43<00:00, 11.52it/s, ce_loss=2.49]\n",
-      "Epoch 5: 100%|████████████████████████████████████████████████| 500/500 [00:43<00:00, 11.56it/s, ce_loss=2.44]\n",
-      "Epoch 6: 100%|█████████████████████████████████████████████████| 500/500 [00:43<00:00, 11.59it/s, ce_loss=2.4]\n",
-      "Epoch 7: 100%|████████████████████████████████████████████████| 500/500 [00:43<00:00, 11.37it/s, ce_loss=2.37]\n",
-      "Epoch 8: 100%|████████████████████████████████████████████████| 500/500 [00:43<00:00, 11.40it/s, ce_loss=2.34]\n",
-      "Epoch 9: 100%|████████████████████████████████████████████████| 500/500 [00:43<00:00, 11.47it/s, ce_loss=2.32]\n",
-      "Epoch 10: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.56it/s, ce_loss=2.29]\n",
-      "Epoch 11: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.42it/s, ce_loss=2.27]\n",
-      "Epoch 12: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.54it/s, ce_loss=2.25]\n",
-      "Epoch 13: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.53it/s, ce_loss=2.24]\n",
-      "Epoch 14: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.49it/s, ce_loss=2.22]\n",
-      "Epoch 15: 100%|███████████████████████████████████████████████| 500/500 [00:44<00:00, 11.34it/s, ce_loss=2.21]\n",
-      "Epoch 16: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.50it/s, ce_loss=2.19]\n",
-      "Epoch 17: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.51it/s, ce_loss=2.18]\n",
-      "Epoch 18: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.54it/s, ce_loss=2.17]\n",
-      "Epoch 19: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.44it/s, ce_loss=2.16]\n",
-      "Epoch 20: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.55it/s, ce_loss=2.15]\n",
-      "Epoch 21: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.48it/s, ce_loss=2.14]\n",
-      "Epoch 22: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.47it/s, ce_loss=2.13]\n",
-      "Epoch 23: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.43it/s, ce_loss=2.12]\n",
-      "Epoch 24: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.45it/s, ce_loss=2.11]\n",
-      "Epoch 25: 100%|████████████████████████████████████████████████| 500/500 [00:43<00:00, 11.45it/s, ce_loss=2.1]\n",
-      "Epoch 26: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.51it/s, ce_loss=2.09]\n",
-      "Epoch 27: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.43it/s, ce_loss=2.08]\n",
-      "Epoch 28: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.37it/s, ce_loss=2.08]\n",
-      "Epoch 29: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.45it/s, ce_loss=2.07]\n",
-      "Epoch 30: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.49it/s, ce_loss=2.06]\n",
-      "Epoch 31: 100%|███████████████████████████████████████████████| 500/500 [00:44<00:00, 11.28it/s, ce_loss=2.06]\n",
-      "Epoch 32: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.71it/s, ce_loss=2.05]\n",
-      "Epoch 33: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.64it/s, ce_loss=2.05]\n",
-      "Epoch 34: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.75it/s, ce_loss=2.04]\n",
-      "Epoch 35: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.79it/s, ce_loss=2.03]\n",
-      "Epoch 36: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.68it/s, ce_loss=2.03]\n",
-      "Epoch 37: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.65it/s, ce_loss=2.02]\n",
-      "Epoch 38: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.65it/s, ce_loss=2.02]\n",
-      "Epoch 39: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.75it/s, ce_loss=2.01]\n",
-      "Epoch 40: 100%|██████████████████████████████████████████████████| 500/500 [00:42<00:00, 11.79it/s, ce_loss=2]\n",
-      "Epoch 41: 100%|██████████████████████████████████████████████████| 500/500 [00:43<00:00, 11.56it/s, ce_loss=2]\n",
-      "Epoch 42: 100%|██████████████████████████████████████████████████| 500/500 [00:42<00:00, 11.72it/s, ce_loss=2]\n",
-      "Epoch 43: 100%|███████████████████████████████████████████████| 500/500 [00:43<00:00, 11.61it/s, ce_loss=1.99]\n",
-      "Epoch 44: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.66it/s, ce_loss=1.99]\n",
-      "Epoch 45: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.74it/s, ce_loss=1.98]\n",
-      "Epoch 46: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.64it/s, ce_loss=1.98]\n",
-      "Epoch 47: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.64it/s, ce_loss=1.97]\n",
-      "Epoch 48: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.66it/s, ce_loss=1.97]\n",
-      "Epoch 49: 100%|███████████████████████████████████████████████| 500/500 [00:42<00:00, 11.77it/s, ce_loss=1.96]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train completed, total time: 2191.36806845665.\n"
-     ]
+   "execution_count": null,
+   "id": "af539d65",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "n_epochs = 50\n",
     "val_interval = 10\n",
@@ -1531,7 +641,7 @@
     "        quantizations = quantizations[:, sequence_ordering]\n",
     "\n",
     "        # Pad input to give start of sequence token\n",
-    "        quantizations = F.pad(quantizations, (1, 0), \"constant\", 256)  # pad with 0 i.e. BOS token\n",
+    "        quantizations = F.pad(quantizations, (1, 0), \"constant\", bos_token)  # pad with BOS token\n",
     "        quantizations = quantizations.long()\n",
     "\n",
     "        quantizations_input = convert_tensor(quantizations[:, :-1], device, non_blocking=True)\n",
@@ -1564,7 +674,7 @@
     "                quantizations = quantizations[:, sequence_ordering]\n",
     "\n",
     "                # Pad input to give start of sequence token\n",
-    "                quantizations = F.pad(quantizations, (1, 0), \"constant\", 256)  # pad with 256 i.e. BOS token\n",
+    "                quantizations = F.pad(quantizations, (1, 0), \"constant\", bos_token)  # pad with BOS token\n",
     "                quantizations = quantizations.long()\n",
     "\n",
     "                quantizations_input = convert_tensor(quantizations[:, :-1], device, non_blocking=True)\n",
@@ -1577,9 +687,9 @@
     "\n",
     "                # Generate a random sample to visualise progress\n",
     "                if val_step == 1:\n",
-    "                    starting_token = 256 * torch.ones((1, 1), device=device)\n",
+    "                    starting_token = vqvae_model.num_embeddings * torch.ones((1, 1), device=device)\n",
     "                    generated_latent = generate(\n",
-    "                        transformer_model, vqvae_model, starting_token, spatial_shape[0] * spatial_shape[1]\n",
+    "                        transformer_model, starting_token, spatial_shape[0] * spatial_shape[1], bos_token\n",
     "                    )\n",
     "                    generated_latent = generated_latent[0]\n",
     "                    vqvae_latent = generated_latent[revert_sequence_ordering]\n",
@@ -1598,29 +708,26 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1100d2c4",
-   "metadata": {},
+   "id": "acea5335",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "### Transformer Loss Curve"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "7fd86e1e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+   "execution_count": null,
+   "id": "c2b6bb2d",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "plt.style.use(\"ggplot\")\n",
     "plt.title(\"Learning Curves\", fontsize=20)\n",
@@ -1642,38 +749,27 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67e4eebf",
-   "metadata": {},
+   "id": "90b5b4b7",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "### Plot evoluation of Generated Samples"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "391f5417",
+   "execution_count": null,
+   "id": "732d7c76",
    "metadata": {
-    "lines_to_next_cell": 2
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1200x3000 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+    "lines_to_next_cell": 2,
+    "pycharm": {
+     "name": "#%%\n"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "# Plot every evaluation as a new line and example as columns\n",
     "val_samples = np.linspace(val_interval, n_epochs, int(n_epochs / val_interval))\n",
@@ -1690,23 +786,33 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fffe05f",
-   "metadata": {},
+   "id": "ddf951ac",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
    "source": [
     "### Generating samples from the trained model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "1fdbabce",
-   "metadata": {},
+   "execution_count": null,
+   "id": "29463149",
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
    "outputs": [],
    "source": [
     "samples = []\n",
     "for i in range(5):\n",
-    "    starting_token = 256 * torch.ones((1, 1), device=device)\n",
-    "    generated_latent = generate(transformer_model, vqvae_model, starting_token, spatial_shape[0] * spatial_shape[1])\n",
+    "    starting_token = vqvae_model.num_embeddings * torch.ones((1, 1), device=device)\n",
+    "    generated_latent = generate(\n",
+    "        transformer_model, starting_token, spatial_shape[0] * spatial_shape[1], bos_token\n",
+    "    )\n",
     "    generated_latent = generated_latent[0]\n",
     "    vqvae_latent = generated_latent[revert_sequence_ordering]\n",
     "    vqvae_latent = vqvae_latent.reshape((1,) + spatial_shape)\n",
@@ -1716,13 +822,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "63bf0adb",
+   "execution_count": 24,
+   "id": "b9ebc8e9",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 5 Axes>"
       ]
@@ -1742,7 +848,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "82147d1f",
+   "id": "24692601",
    "metadata": {},
    "source": [
     "### Cleanup data directory\n",
@@ -1752,8 +858,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "2bb33a5d",
+   "execution_count": 25,
+   "id": "28dd0e26",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1786,4 +892,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.py b/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.py
index 4d5bfc97..6bee24a2 100644
--- a/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.py
+++ b/tutorials/generative/2d_vqvae_transformer/2d_vqvae_transformer_tutorial.py
@@ -16,17 +16,18 @@
 # %% [markdown]
 # # Vector Quantized Variational Autoencoders and Transformers with MedNIST Dataset
 #
-# This tutorial illustrates how to use MONAI for training a Vector Quantized Variational Autoencoder (VQVAE)[1] and a transformer model on 2D images.
+# This tutorial illustrates how to use MONAI for training a Vector Quantized Variational Autoencoder (VQVAE)[1,2] and a transformer model on 2D images.
 #
 # This is a two step process:
 # - We will train our VQVAE model to be able to reconstruct the input images.
 # - This will be followed by using the trained VQVAE model to encode images to feed into the transformer network to train.
 #
-# We will work with the MedNIST dataset available on MONAI
-# (https://docs.monai.io/en/stable/apps.html#monai.apps.MedNISTDataset). In order to train faster, we will select just one of the available classes ("HeadCT"), resulting in a training set with 7999 2D images.
+# We will work with the [MedNIST dataset](https://docs.monai.io/en/stable/apps.html#monai.apps.MedNISTDataset) available on MONAI. In order to train faster, we will select just one of the available classes ("HeadCT"), resulting in a training set with 7999 2D images.
 #
 # [1] - [Oord et al. "Neural Discrete Representation Learning"](https://arxiv.org/abs/1711.00937)
 #
+# [2] - [Tudosiu et al. "Morphology-Preserving Autoregressive 3D Generative Modelling of the Brain"](https://arxiv.org/abs/2209.03177)
+#
 #
 # ### Setup imports
 
@@ -105,7 +106,7 @@
     ]
 )
 train_ds = Dataset(data=train_datalist, transform=train_transforms)
-train_loader = DataLoader(train_ds, batch_size=128, shuffle=True, num_workers=4)
+train_loader = DataLoader(train_ds, batch_size=128, shuffle=True, num_workers=4, persistent_workers=True)
 
 # %% [markdown]
 # ### Visualse some examples from the dataset
@@ -132,7 +133,7 @@
     ]
 )
 val_ds = Dataset(data=val_datalist, transform=val_transforms)
-val_loader = DataLoader(val_ds, batch_size=128, shuffle=True, num_workers=4)
+val_loader = DataLoader(val_ds, batch_size=128, shuffle=True, num_workers=4, persistent_workers=True)
 
 # %% [markdown]
 # ## VQVAE Training
@@ -156,7 +157,7 @@
     num_embeddings=256,
     embedding_dim=32,
 )
-vqvae_model.to(device)
+vqvae_model = vqvae_model.to(device)
 
 # %%
 optimizer = torch.optim.Adam(params=vqvae_model.parameters(), lr=1e-4)
@@ -296,8 +297,8 @@
 # We can use the same dataloader with augmentations as used for training the VQVAE model. However given the memory intensive nature of Transformer models we will need to reduce the batch size
 
 # %%
-train_loader = DataLoader(train_ds, batch_size=16, shuffle=True, num_workers=4)
-val_loader = DataLoader(val_ds, batch_size=16, shuffle=True, num_workers=4)
+train_loader = DataLoader(train_ds, batch_size=16, shuffle=True, num_workers=4, persistent_workers=True)
+val_loader = DataLoader(val_ds, batch_size=16, shuffle=True, num_workers=4, persistent_workers=True)
 
 # %% [markdown]
 # ### Latent sequence ordering
@@ -318,6 +319,14 @@
 revert_sequence_ordering = ordering.get_revert_sequence_ordering()
 
 
+# %% [markdown]
+# ### Begin of sentence token (BOS)
+#
+# After we transform the data into a 1D representation, we need to define a value token to define the begining of the sequence (a.k.a., BOS token in NLP). This way, when we feed it to the transformer, the transformer will predict what is the value of the first valid token in the sequence. Since in the VQ-VAE, we are using the token values from 0 to 255 to define the elements of the codebook, here we will specify the next value as the be BOS, i.e. 256.
+
+# %%
+bos_token = 256
+
 # %% [markdown]
 # ## Define Network, optimizer and losses
 
@@ -331,7 +340,7 @@
     attn_layers_depth=12,
     attn_layers_heads=8,
 )
-transformer_model.to(device)
+transformer_model = transformer_model.to(device)
 
 # %%
 optimizer = torch.optim.Adam(params=transformer_model.parameters(), lr=5e-4)
@@ -344,7 +353,7 @@
 
 # %%
 @torch.no_grad()
-def generate(net, vqvae_model, starting_tokens, seq_len, **kwargs):
+def generate(net, starting_tokens, seq_len, bos_token):
     progress_bar = iter(range(seq_len))
 
     latent_seq = starting_tokens.long()
@@ -364,7 +373,7 @@ def generate(net, vqvae_model, starting_tokens, seq_len, **kwargs):
         # apply softmax to convert logits to (normalized) probabilities
         probs = F.softmax(logits, dim=-1)
         # remove the chance to be sampled the BOS token
-        probs[:, vqvae_model.num_embeddings - 1] = 0
+        probs[:, bos_token] = 0
 
         # sample from the distribution
         idx_next = torch.multinomial(probs, num_samples=1)
@@ -377,7 +386,7 @@ def generate(net, vqvae_model, starting_tokens, seq_len, **kwargs):
 
 # %% [markdown]
 # ### Transformer Model Training
-# We will train the model for 100 epochs
+# We will train the model for 50 epochs
 
 # %%
 n_epochs = 50
@@ -401,7 +410,7 @@ def generate(net, vqvae_model, starting_tokens, seq_len, **kwargs):
         quantizations = quantizations[:, sequence_ordering]
 
         # Pad input to give start of sequence token
-        quantizations = F.pad(quantizations, (1, 0), "constant", 256)  # pad with 0 i.e. BOS token
+        quantizations = F.pad(quantizations, (1, 0), "constant", bos_token)  # pad with BOS token
         quantizations = quantizations.long()
 
         quantizations_input = convert_tensor(quantizations[:, :-1], device, non_blocking=True)
@@ -434,7 +443,7 @@ def generate(net, vqvae_model, starting_tokens, seq_len, **kwargs):
                 quantizations = quantizations[:, sequence_ordering]
 
                 # Pad input to give start of sequence token
-                quantizations = F.pad(quantizations, (1, 0), "constant", 256)  # pad with 256 i.e. BOS token
+                quantizations = F.pad(quantizations, (1, 0), "constant", bos_token)  # pad with BOS token
                 quantizations = quantizations.long()
 
                 quantizations_input = convert_tensor(quantizations[:, :-1], device, non_blocking=True)
@@ -447,9 +456,9 @@ def generate(net, vqvae_model, starting_tokens, seq_len, **kwargs):
 
                 # Generate a random sample to visualise progress
                 if val_step == 1:
-                    starting_token = 256 * torch.ones((1, 1), device=device)
+                    starting_token = vqvae_model.num_embeddings * torch.ones((1, 1), device=device)
                     generated_latent = generate(
-                        transformer_model, vqvae_model, starting_token, spatial_shape[0] * spatial_shape[1]
+                        transformer_model, starting_token, spatial_shape[0] * spatial_shape[1], bos_token
                     )
                     generated_latent = generated_latent[0]
                     vqvae_latent = generated_latent[revert_sequence_ordering]
@@ -509,8 +518,10 @@ def generate(net, vqvae_model, starting_tokens, seq_len, **kwargs):
 # %%
 samples = []
 for i in range(5):
-    starting_token = 256 * torch.ones((1, 1), device=device)
-    generated_latent = generate(transformer_model, vqvae_model, starting_token, spatial_shape[0] * spatial_shape[1])
+    starting_token = vqvae_model.num_embeddings * torch.ones((1, 1), device=device)
+    generated_latent = generate(
+        transformer_model, starting_token, spatial_shape[0] * spatial_shape[1], bos_token
+    )
     generated_latent = generated_latent[0]
     vqvae_latent = generated_latent[revert_sequence_ordering]
     vqvae_latent = vqvae_latent.reshape((1,) + spatial_shape)