diff --git a/example/language_model/pytorch/cbow.ipynb b/example/language_model/pytorch/cbow.ipynb
index cb4ac3c92..fd818c445 100644
--- a/example/language_model/pytorch/cbow.ipynb
+++ b/example/language_model/pytorch/cbow.ipynb
@@ -116,6 +116,73 @@
     "print(f'Context: {context}\\n')\n",
     "print(f'Prediction: {index_to_word[torch.argmax(a[0]).item()]}')"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cpu\n",
+      "mps:0\n",
+      "Total time with cpu (10000): 0.0005950000000005673\n",
+      "Total time with cpu (100000000): 0.0002990000000000492\n",
+      "available\n",
+      "Total time with gpu (10000): 0.0002899999999996794\n",
+      "Total time with gpu (100000000): 0.0003420000000007306\n"
+     ]
+    }
+   ],
+   "source": [
+    "from time import process_time\n",
+    "import torch\n",
+    "\n",
+    "print(torch.get_default_device())\n",
+    "torch.set_default_device('mps')\n",
+    "print(torch.get_default_device())\n",
+    "def testgpu():\n",
+    "    if torch.backends.mps.is_available():\n",
+    "        print(\"available\")\n",
+    "        mps_device = torch.device(\"mps\")\n",
+    "    t0 = process_time()\n",
+    "    x = torch.ones(n1, device=mps_device)\n",
+    "    y = x + torch.rand(n1, device=mps_device)\n",
+    "    t1 = process_time()\n",
+    "    print(f\"Total time with gpu ({n1}): {t1-t0}\")\n",
+    "    t0 = process_time()\n",
+    "    x = torch.ones(n2, device=mps_device)\n",
+    "    y = x + torch.rand(n2, device=mps_device)\n",
+    "    t1 = process_time()\n",
+    "    print(f\"Total time with gpu ({n2}): {t1-t0}\")\n",
+    "\n",
+    "def testcpu():\n",
+    "    t0 = process_time()\n",
+    "    x = torch.ones(n1)\n",
+    "    y = x + torch.rand(n1)\n",
+    "    t1 = process_time()\n",
+    "    print(f\"Total time with cpu ({n1}): {t1-t0}\")\n",
+    "    t0 = process_time()\n",
+    "    x = torch.ones(n2)\n",
+    "    y = x + torch.rand(n2)\n",
+    "    t1 = process_time()\n",
+    "    print(f\"Total time with cpu ({n2}): {t1-t0}\")\n",
+    "\n",
+    "if __name__ == '__main__':\n",
+    "    n1 = 10000\n",
+    "    n2 = 100000000\n",
+    "    testcpu()\n",
+    "    testgpu()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/example/language_model/pytorch/next_word_prediction/rnn.ipynb b/example/language_model/pytorch/next_word_prediction/rnn.ipynb
index afb5935b3..03b0307a2 100644
--- a/example/language_model/pytorch/next_word_prediction/rnn.ipynb
+++ b/example/language_model/pytorch/next_word_prediction/rnn.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [
     {
@@ -22,6 +22,14 @@
     "import torch.nn.functional as F\n",
     "import numpy as np\n",
     "\n",
+    "# For M1 macs\n",
+    "# TODO: Why is it 10x slower than cpu?\n",
+    "# if torch.backends.mps.is_available():\n",
+    "#     print(\"Using M1 GPU\")\n",
+    "#     torch.set_default_device('mps')\n",
+    "#     mps_device = torch.device(\"mps\")\n",
+    "\n",
+    "torch.set_default_device('cpu')\n",
     "with open(\"./data.txt\", \"r\") as f:\n",
     "    data = f.read().split(\"\\n\")\n",
     "\n",
@@ -42,7 +50,7 @@
     "chars = set(char for line in data for char in line)\n",
     "num_of_chars = len(chars)\n",
     "print(\"Number of chars\", num_of_chars)\n",
-    "batch_size = 1\n",
+    "batch_size = 32\n",
     "\n",
     "def word2tensor(word):\n",
     "    # That extra 1 dimension is because PyTorch assumes everything is in batches - we’re just using a batch size of 1 here.\n",
@@ -54,15 +62,21 @@
     "def tensor2word(tensor):\n",
     "    return index2word[tensor.argmax().item()]\n",
     "\n",
-    "# def categoryFromOutput(output):\n",
-    "#     _, top_i = output.topk(1)\n",
-    "#     category_i = top_i[0][0].item()\n",
-    "#     return num_of_words[category_i], category_i\n"
+    "# Creating data for training \n",
+    "HIDDEN_LAYER_SIZE = 2\n",
+    "CONTEXT_WINDOW = 3\n",
+    "data_context = []\n",
+    "\n",
+    "for line in data:\n",
+    "    line = line.split()\n",
+    "    n = len(line)\n",
+    "    for i in range(n - CONTEXT_WINDOW):\n",
+    "        data_context.append((line[i: i + CONTEXT_WINDOW], line[i + CONTEXT_WINDOW]))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,93 +89,66 @@
     "        self.input2hidden = nn.Linear(input_size, hidden_size)\n",
     "        self.hidden2hidden = nn.Linear(hidden_size, hidden_size)\n",
     "        self.hidden2output = nn.Linear(hidden_size, output_size)\n",
-    "        self.softmax = nn.LogSoftmax(dim=1)\n",
+    "        self.log_softmax = nn.LogSoftmax(dim=2)\n",
     "\n",
     "    def forward(self, input, hidden):\n",
-    "        print(\"Start\", np.nonzero(input.detach().numpy())[-1], hidden)\n",
+    "        # print(\"Start\", np.nonzero(input.detach().numpy())[-1], hidden)\n",
     "        input = self.input2hidden(input)\n",
-    "        print(\"After first layer\", np.nonzero(input.detach().numpy())[-1], hidden)\n",
-    "        print(\"1) After input to hidden\", input.shape, hidden.shape)\n",
+    "        # print(\"After first layer\", np.nonzero(input.detach().numpy())[-1], hidden)\n",
+    "        # print(\"1) After input to hidden\", input.shape, hidden.shape)\n",
     "\n",
     "        hidden = self.hidden2hidden(hidden)\n",
-    "        print(\"2) After hidden to hidden\", hidden.shape)\n",
+    "        # print(\"2) After hidden to hidden\", hidden.shape)\n",
     "\n",
     "        hidden = F.tanh(input + hidden)\n",
-    "        print(\"3) After tanh\", input.shape, hidden.shape)\n",
+    "        # print(\"3) After tanh\", input.shape, hidden.shape)\n",
     "\n",
     "        output = self.hidden2output(hidden)\n",
-    "        print(\"4) After hidden to output\", output.shape)\n",
+    "        # print(\"4) After hidden to output\", output.shape)\n",
     "\n",
-    "        output = self.softmax(output)\n",
-    "        print(\"5) After softmax\", output.shape)\n",
+    "        output = self.log_softmax(output)\n",
+    "        # print(\"5) After softmax\", output.shape)\n",
     "        return output, hidden\n",
     "\n",
-    "    def init_hidden(self, batch_size=batch_size):\n",
-    "        return torch.zeros(batch_size, self.hidden_size)\n",
-    "\n",
-    "\n",
-    "# Creating data for training \n",
-    "HIDDEN_LAYER_SIZE = 2\n",
-    "CONTEXT_WINDOW = 3\n",
-    "data_context = []\n",
-    "\n",
-    "for line in data:\n",
-    "    line = line.split()\n",
-    "    n = len(line)\n",
-    "    for i in range(n - CONTEXT_WINDOW):\n",
-    "        data_context.append((line[i: i + CONTEXT_WINDOW], line[i + CONTEXT_WINDOW]))\n"
+    "    def init_hidden(self, batch_size):\n",
+    "        return torch.zeros(batch_size, self.hidden_size)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 162,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Start [4390] tensor([[0., 0.]])\n",
-      "After first layer [0 1] tensor([[0., 0.]])\n",
-      "1) After input to hidden torch.Size([1, 1, 2]) torch.Size([1, 2])\n",
-      "2) After hidden to hidden torch.Size([1, 2])\n",
-      "3) After tanh torch.Size([1, 1, 2]) torch.Size([1, 1, 2])\n",
-      "4) After hidden to output torch.Size([1, 1, 8600])\n",
-      "5) After softmax torch.Size([1, 1, 8600])\n",
-      "Start [4993] tensor([[[0.4916, 0.1534]]], grad_fn=<TanhBackward0>)\n",
-      "After first layer [0 1] tensor([[[0.4916, 0.1534]]], grad_fn=<TanhBackward0>)\n",
-      "1) After input to hidden torch.Size([1, 1, 2]) torch.Size([1, 1, 2])\n",
-      "2) After hidden to hidden torch.Size([1, 1, 2])\n",
-      "3) After tanh torch.Size([1, 1, 2]) torch.Size([1, 1, 2])\n",
-      "4) After hidden to output torch.Size([1, 1, 8600])\n",
-      "5) After softmax torch.Size([1, 1, 8600])\n",
-      "Start [6012] tensor([[[0.2191, 0.1146]]], grad_fn=<TanhBackward0>)\n",
-      "After first layer [0 1] tensor([[[0.2191, 0.1146]]], grad_fn=<TanhBackward0>)\n",
-      "1) After input to hidden torch.Size([1, 1, 2]) torch.Size([1, 1, 2])\n",
-      "2) After hidden to hidden torch.Size([1, 1, 2])\n",
-      "3) After tanh torch.Size([1, 1, 2]) torch.Size([1, 1, 2])\n",
-      "4) After hidden to output torch.Size([1, 1, 8600])\n",
-      "5) After softmax torch.Size([1, 1, 8600])\n",
-      "Epoch 1/1 Loss: 0.0\n"
+      "Epoch 1/10 Loss: 564788.5739929676\n",
+      "Epoch 2/10 Loss: 521980.39820051193\n",
+      "Epoch 3/10 Loss: 511599.02045464516\n",
+      "Epoch 4/10 Loss: 504765.9905807972\n",
+      "Epoch 5/10 Loss: 499933.83950662613\n",
+      "Epoch 6/10 Loss: 496379.2070118189\n",
+      "Epoch 7/10 Loss: 493662.4143565893\n",
+      "Epoch 8/10 Loss: 491526.34502995014\n",
+      "Epoch 9/10 Loss: 489797.94599330425\n",
+      "Epoch 10/10 Loss: 488364.6461007595\n"
      ]
     }
    ],
    "source": [
-    "\n",
     "rnn = RNN(num_of_words, HIDDEN_LAYER_SIZE, num_of_words)\n",
     "lr = 0.01\n",
     "criterion = nn.NLLLoss()\n",
     "optimizer = torch.optim.SGD(rnn.parameters(), lr=lr)\n",
-    "epochs = 1\n",
+    "epochs = 10\n",
     "\n",
-    "data_context = data_context[:1]\n",
     "for epoch in range(epochs):\n",
     "    total_loss = 0\n",
     "\n",
     "    for context, target in data_context: \n",
     "        hidden = rnn.init_hidden()\n",
     "        rnn.zero_grad()\n",
-    "        # print(context, target)\n",
     "        for word in context:\n",
     "            output, hidden = rnn(word2tensor(word), hidden)\n",
     "        \n",
@@ -169,70 +156,161 @@
     "        # Get rid of the batch size dimension\n",
     "        output = output.squeeze(0)\n",
     "        target_tensor = torch.tensor([word2index[target]], dtype=torch.long)\n",
+    "        # Why the fuck do we need to convert it to LongTensor?\n",
     "        loss = criterion(output, target_tensor)\n",
     "\n",
     "        loss.backward()\n",
+    "        # print(loss.item())\n",
     "        optimizer.step()\n",
     "        total_loss += loss.item()\n",
     "\n",
-    "        # Dump output and target tensor to a file output.txt\n",
-    "        with open(\"output.txt\", \"a\") as f:\n",
-    "            f.write(\"Predicted tensor: \")\n",
-    "            output_array = output.detach().numpy()\n",
-    "            # Find indices where values are not 0\n",
-    "            nonzero_indices = np.nonzero(output_array)[-1]\n",
-    "            f.write(str(nonzero_indices) + \"\\n\")\n",
+    "        # For debugging purposes: Dump output and target tensor to file output.txt\n",
+    "\n",
+    "        # import sys\n",
+    "        # np.set_printoptions(threshold=sys.maxsize)\n",
+    "        # with open(\"output.txt\", \"a\") as f:\n",
+    "        #     f.write(\"Predicted tensor: \")\n",
+    "        #     output_array = output.detach().numpy()\n",
+    "        #     # Find indices where values are not 0\n",
+    "        #     # nonzero_indices = np.nonzero(output_array)[-1]\n",
+    "        #     print(\"Output array\", output_array)\n",
+    "        #     f.write(str(output_array) + \"\\n\")\n",
     "\n",
-    "            f.write(\"Target tensor: \" + str(target_tensor.detach().numpy()) + \" \" + str(target) + \"\\n\")\n",
+    "        #     print(\"Target tensor\", target_tensor.detach().numpy())\n",
+    "        #     f.write(\"Target tensor: \" + str(target_tensor.detach().numpy()) + \" \" + str(target) + \"\\n\")\n",
     "\n",
-    "            f.write(\"Loss: \" + str(loss.item()) + \"\\n\\n\")\n",
+    "        #     print(\"Loss:\", loss.item())\n",
+    "        #     f.write(\"Loss: \" + str(loss.item()) + \"\\n\\n\")\n",
     "\n",
     "    print(f\"Epoch {epoch + 1}/{epochs} Loss: {total_loss}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Expected input batch_size (32) to match target batch_size (1).",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[171], line 38\u001b[0m\n\u001b[1;32m     36\u001b[0m     output \u001b[38;5;241m=\u001b[39m output\u001b[38;5;241m.\u001b[39msqueeze(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     37\u001b[0m     target_tensor \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([word2index[target]], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong)\n\u001b[0;32m---> 38\u001b[0m     loss \u001b[38;5;241m=\u001b[39m \u001b[43mcriterion\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_tensor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     39\u001b[0m     batch_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\n\u001b[1;32m     41\u001b[0m batch_loss\u001b[38;5;241m.\u001b[39mbackward()\n",
+      "File \u001b[0;32m~/Desktop/projects/GigaTorch/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Desktop/projects/GigaTorch/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/Desktop/projects/GigaTorch/.venv/lib/python3.10/site-packages/torch/nn/modules/loss.py:216\u001b[0m, in \u001b[0;36mNLLLoss.forward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor, target: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 216\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnll_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreduction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduction\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Desktop/projects/GigaTorch/.venv/lib/python3.10/site-packages/torch/nn/functional.py:2747\u001b[0m, in \u001b[0;36mnll_loss\u001b[0;34m(input, target, weight, size_average, ignore_index, reduce, reduction)\u001b[0m\n\u001b[1;32m   2705\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Compute the negative log likelihood loss.\u001b[39;00m\n\u001b[1;32m   2706\u001b[0m \n\u001b[1;32m   2707\u001b[0m \u001b[38;5;124;03mSee :class:`~torch.nn.NLLLoss` for details.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2744\u001b[0m \u001b[38;5;124;03m    >>> output.backward()\u001b[39;00m\n\u001b[1;32m   2745\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   2746\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_variadic(\u001b[38;5;28minput\u001b[39m, target, weight):\n\u001b[0;32m-> 2747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mhandle_torch_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2748\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnll_loss\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2749\u001b[0m \u001b[43m        \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2750\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2751\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2752\u001b[0m \u001b[43m        \u001b[49m\u001b[43mweight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2753\u001b[0m \u001b[43m        \u001b[49m\u001b[43msize_average\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize_average\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2754\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2755\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreduce\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2756\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreduction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreduction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2757\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2758\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size_average \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m reduce \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   2759\u001b[0m     reduction \u001b[38;5;241m=\u001b[39m _Reduction\u001b[38;5;241m.\u001b[39mlegacy_get_string(size_average, reduce)\n",
+      "File \u001b[0;32m~/Desktop/projects/GigaTorch/.venv/lib/python3.10/site-packages/torch/overrides.py:1619\u001b[0m, in \u001b[0;36mhandle_torch_function\u001b[0;34m(public_api, relevant_args, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1615\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _is_torch_function_mode_enabled():\n\u001b[1;32m   1616\u001b[0m     \u001b[38;5;66;03m# if we're here, the mode must be set to a TorchFunctionStackMode\u001b[39;00m\n\u001b[1;32m   1617\u001b[0m     \u001b[38;5;66;03m# this unsets it and calls directly into TorchFunctionStackMode's torch function\u001b[39;00m\n\u001b[1;32m   1618\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m _pop_mode_temporarily() \u001b[38;5;28;01mas\u001b[39;00m mode:\n\u001b[0;32m-> 1619\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mmode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__torch_function__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpublic_api\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1620\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[1;32m   1621\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[0;32m~/Desktop/projects/GigaTorch/.venv/lib/python3.10/site-packages/torch/utils/_device.py:78\u001b[0m, in \u001b[0;36mDeviceContext.__torch_function__\u001b[0;34m(self, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01min\u001b[39;00m _device_constructors() \u001b[38;5;129;01mand\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdevice\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     77\u001b[0m     kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdevice\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice\n\u001b[0;32m---> 78\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Desktop/projects/GigaTorch/.venv/lib/python3.10/site-packages/torch/nn/functional.py:2760\u001b[0m, in \u001b[0;36mnll_loss\u001b[0;34m(input, target, weight, size_average, ignore_index, reduce, reduction)\u001b[0m\n\u001b[1;32m   2758\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size_average \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m reduce \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   2759\u001b[0m     reduction \u001b[38;5;241m=\u001b[39m _Reduction\u001b[38;5;241m.\u001b[39mlegacy_get_string(size_average, reduce)\n\u001b[0;32m-> 2760\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_nn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnll_loss_nd\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_Reduction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_enum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreduction\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mValueError\u001b[0m: Expected input batch_size (32) to match target batch_size (1)."
+     ]
+    }
+   ],
+   "source": [
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "\n",
+    "class CustomDataset(Dataset):\n",
+    "    def __init__(self, data_context):\n",
+    "        self.data = data_context\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.data)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        return self.data[idx]\n",
+    "\n",
+    "# Assuming data_context is a list of tuples (context, target)\n",
+    "dataset = CustomDataset(data_context)\n",
+    "batch_size = 32  # Specify your desired batch size\n",
+    "dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n",
+    "\n",
+    "rnn2 = RNN(num_of_words, HIDDEN_LAYER_SIZE, num_of_words)\n",
+    "lr = 0.02\n",
+    "criterion = nn.NLLLoss()\n",
+    "optimizer = torch.optim.SGD(rnn2.parameters(), lr=lr)\n",
+    "epochs = 100\n",
+    "\n",
+    "for epoch in range(epochs):\n",
+    "    total_loss = 0\n",
+    "\n",
+    "    for batch_idx, (contexts, targets) in enumerate(dataloader):\n",
+    "        optimizer.zero_grad()\n",
+    "        batch_loss = 0\n",
+    "\n",
+    "        for context, target in zip(contexts, targets):\n",
+    "            hidden = rnn2.init_hidden(batch_size=batch_size)\n",
+    "            for word in context:\n",
+    "                output, hidden = rnn2(word2tensor(word), hidden)\n",
+    "\n",
+    "            output = output.squeeze(0)\n",
+    "            target_tensor = torch.tensor([word2index[target]], dtype=torch.long)\n",
+    "            loss = criterion(output, target_tensor)\n",
+    "            batch_loss += loss\n",
+    "\n",
+    "        batch_loss.backward()\n",
+    "        optimizer.step()\n",
+    "        total_loss += batch_loss.item()\n",
+    "\n",
+    "    print(f\"Epoch {epoch + 1}/{epochs} Loss: {total_loss}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'reaches', 'bulky', 'concentrate', 'junior', 'butted', 'simplifies', 'returned', 'trunk', 'infringement', 'hear', 'knowfaddy', 'songs', 'hugh', 'eggs', 'slurred', 'donation', 'eligible', 'lure', 'aloud', 'signed', 'protruding', 'though', 'splendidly', 'disclaimers', 'suite', 'hague', 'wearisome', 'gasfitters', 'early', 'greasy', 'occurrences', 'curse', 'carte', 'whine', 'adapted', 'brides', 'convenient', 'drunkenlooking', 'hay', 'melbourne', 'beads', 'accessory', 'retorted', 'patienceneville', 'stealthily', 'wrack', 'reading', 'mary', 'voicehave', 'dishonoured', 'nip', 'men', 'guilty', 'suggestiveness', 'panoply', 'tossed', 'sunk', 'disposition', 'haste', 'secretthe', 'implored', 'incognito', 'notion', 'attracted', 'popular', 'sidealley', 'plumbers', 'additional', 'fluffy', 'persevering', 'americans', 'close', 'pulling', 'lordship', 'accomplice', 'skirmishes', 'partially', 'lie', 'slim', 'borrow', 'glowing', 'magistrate', 'families', 'keeps', 'manual', 'peeping', 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'broadened', 'skin', 'stocked', 'sottish', 'cosmopolitan', 'backed', 'seastories', 'fruits', 'halfcolumn', 'awoke', 'drowned', 'determine', 'trifling', 'towns', 'vital', 'parish', 'abjure', 'againjust', 'panel', 'robberies', 'enters', 'laying', 'morning', 'chinchilla', 'malay', 'finds', 'mind', 'badly', 'sideboard', 'altar', 'hers', 'skill', 'chased', 'notably', 'attainments', 'treat', 'dignity', 'lays', 'executive', 'bee', 'operatic', 'frost', 'dropping', 'lingering', 'pence', 'flung', 'twentyfour', 'meadow', 'sit', 'dense', 'finished', 'point', 'miles', 'catastrophe', 'mostly', 'including', 'screaming', 'gibe', 'overtook', 'firm', 'gun', 'bless', 'ladyships', 'hope', 'unofficial', 'shoulder', 'reedgirt', 'along', 'attics', 'outoftheway', 'conundrums', 's', 'read', 'twelvemile', 'quicker', 'takings', 'support', 'discover', 'chapter', 'imprisoned', 'narrowed', 'volume', 'fullsailed', 'interest', 'seldom', 'ventilator', 'silently', 'bushes', 'briar', 'stableboy', 'training', 'consults', 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'heartily', 'coloured', 'lad', 'demonill', 'rest', 'odessa', 'sidetable', 'turns', 'bradstreet', 'fierce', 'pips', 'stepped', 'resolute', 'selflighting', 'partner', 'bs', 'smoking', 'lothman', 'freemasonry', 'bark', 'death', 'submitted', 'parapet', 'indoors', 'clears', 'confronted', 'feeling', 'risers', 'plans', 'hardly', 'enables', 'agreed', 'monogram', 'vagueness', 'dwell', 'inform', 'fronts', 'compunction', 'footsteps', 'bellpull', 'bedroom', 'linoleum', 'methods', 'singlehanded', 'mess', 'shift', 'munro', 'united', 'modest', 'beryl', 'driven', 'brick', 'member', 'usual', 'sometimes', 'please', 'hand', 'nearly', 'suppose', 'premises', 'suspended', 'catch', 'intensified', 'enter', 'outskirts', 'gasflare', 'cabby', 'intelligent', 'tying', 'bridal', 'concentrated', 'realistic', 'lee', 'comforted', 'tinkers', 'middleaged', 'observant', 'theft', 'tortured', 'estate', 'graveldrive', 'fancy', 'strengthen', 'seats', 'hood', 'pokers', 'investigated', 'goodnight', 'antics', 'watered', 'surveyed', 'clouds', 'snarl', 'sleepily', 'unacquainted', 'weaker', 'begun', 'conclusions', 'office', 'establish', 'langham', 'fumes', 'discovery', 'possessed', 'son', 'suspect', 'treachery', 'james', 'sigh', 'fly', 'hastening', 'sin', 'regretted', 'leather', 'expression', 'rescue', 'services', 'mastery', 'commit', 'build', 'comparatively', 'oak', 'hindrance', 'especially', 'banker', 'full', 'crinkled', 'saw', 'weighed', 'popes', 'exaustralian', 'wretched', 'enemys', 'forfeit', 'commands', 'difficulty', 'nice', 'spoken', 'idea', 'forgiveness', 'borne', 'newly', 'proving', 'today', 'clump', 'southern', 'tendencies', 'soda', 'moistened', 'clapped', 'writhing', 'londonquite', 'whishing', 'withdrawn', 'seal', 'countrytown', 'exacting', 'poetry', 'paddington', 'flamecoloured', 'merchantability', 'reserve', 'brow', 'hotblooded', 'clearing', 'particulars', 'bride', 'resources', 'dnouement', 'bristol', 'imitated', 'beyond', 'costers', 'portly', 'ugliness', 'roots', 'locket', 'shrillya', 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'borders', 'respectable', 'trincomalee', 'whistles', 'suit', 'spots', 'gift', 'stump', 'points', 'opulence', 'thicksoled', 'twisted', 'were', 'perpetrated', 'gravity', 'notepaper', 'bills', 'mine', 'lust', 'general', 'probable', 'characterdummy', 'slumber', 'immensely', 'staples', 'failing', 'depressed', 'splashing', 'tune', 'maids', 'telling', 'places', 'merchantman', 'missing', 'force', 'yearand', 'annual', 'reports', 'hinges', 'creases', 'youth', 'buttoned', 'figured', 'fell', 'finder', 'twinkling', 'frighten', 'ransacked', 'spring', 'invited', 'britain', 'resist', 'authoritative', 'preliminary', 'shawl', 'continued', 'pheasant', 'smack', 'accurately', 'invaluable', 'membra', 'lit', 'providing', 'contemptuous', 'fishmonger', 'backgammon', 'isolation', 'emotion', 'lefthanded', 'gaining', 'appearing', 'rely', 'enthusiasm', 'college', 'propriety', 'consult', 'won', 'move', 'many', 'complexion', 'darted', 'conducted', 'lancaster', 'proprietor', 'affecting', 'id', 'should', 'hearts', 'ghastly', 'gentlemanly', 'safety', 'possessions', 'heroic', 'proofs', 'buzzing', 'thrust', 'revenge', 'dressing', 'gossips', 'fortnight', 'bandage', 'fifty', 'sweetness', 'lebanon', 'charing', 'fortyone', 'strayed', 'from', 'hastily', 'eat', 'divinedin', 'wellgroomed', 'albert', 'mornings', 'loungers', 'cornwall', 'used', 'mention', 'outcry', 'cousins', 'country', 'degree', 'noted', 'record', 'at', 'overhauled', 'mouth', 'references', 'while', 'donate', 'relations', 'blazing', 'adventures', 'actions', 'settle', 'dress', 'theorise', 'inextricable', 'solvedwhat', 'exclusion', 'convoy', 'blandly', 'straw', 'toothbrush', 'can', 'meshes', 'luxurious', 'tobacconist', 'found', 'metropolis', 'crocuses', 'authority', 'rightly', 'entered', 'recognise', 'search', 'welllit', 'tied', 'bandages', 'fewer', 'butler', 'monotony', 'piquant', 'coolness', 'sadfaced', 'lasting', 'hearing', 'examined', 'assured', 'murders', 'bizarre', 'anteroom', 'moving', 'employees', 'reasoner', 'glancing', 'lest', 'mccauley', 'sore', 'clasping', 'ruefully', 'ginshop', 'arrange', 'murder', 'ix', 'fearless', 'distribute', 'descent', 'physical', 'encamp', 'meetings', 'traffic', 'waylaid', 'filling', 'warehouse', 'hubbub', 'hotheaded', 'obtaining', 'crumbly', 'stone', 'ambitious', 'weird', 'xi', 'mortals', 'wind', 'persistently', 'bones', 'gasjet', 'stumbled', 'fulfilment', 'approach', 'cotton', 'finer', 'trusted', 'thirtyseven', 'speaks', 'modern', 'valid', 'destroyed', 'splendour', 'glove', 'cruel', 'animal', 'lent', 'horror', 'shaken', 'mile', 'uncomfortable', 'commonplace', 'disappearance', 'converse', 'donors', 'stride', 'trout', 'mates', 'service', 'maidsjoined', 'louder', 'shirtsleeve', 'administration', 'luncheon', 'information', 'pallet', 'boswell', 'chins', 'gaping', 'cousin', 'agitated', 'knows', 'awake', 'barrow', 'our', 'impulsively', 'alsobut', 'straining', 'second', 'filled', 'entangled', 'advantages', 'furtive', 'oillamp', 'whose', 'indulged', 'lured', 'surpliced', 'disagreeable', 'normal', 'e', 'convince', 'november', 'scheming', 'prompt', 'neat', 'stuff', 'slighted', 'brightly', 'accepted', 'composed', 'casting', 'uproar', 'everything', 'rising', 'slopshop', 'railings', 'combined', 'present', 'gleaming', 'waxed', 'westbury', 'rambling', 'guessed', 'town', 'unheeded', 'hurts', 'shimmering', 'goals', 'rule', 'ado', 'cobwebby', 'coldblooded', 'beginnings', 'and', 'dorans', 'figure', 'swim', 'spend', 'swaying', 'ezekiah', 'beneath', 'field', 'thats', 'series', 'family', 'lieu', 'pondicherry', 'arrived', 'perceived', 'specific', 'told', 'oldest', 'ft', 'steps', 'tattoo', 'inspection', 'successful', 'speedy', 'chink', 'mortimers', 'sodden', 'views', 'receded', 'acts', 'beige', 'quivering', 'things', 'clinked', 'linked', 'disadvantages', 'parley', 'confine', 'pledged', 'rival', 'seared', 'expend', 'shook', 'bechers', 'dustcoat', 'rueful', 'spectacles', 'tut', 'sunlight', 'lying', 'division', 'swimming', 'forget', 'lids', 'noiselessly', 'murderers', 'p', 'resting', 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'ungovernable', 'body', 'heather', 'iota', 'meeting', 'uncertain', 'fangs', 'supporters', 'goodnatured', 'hercules', 'elbows', 'whoa', 'depends', 'substitution', 'negro', 'drunkard', 'rs', 'sunburnt', 'mistress', 'theory', 'duplicates', 'masked', 'strip', 'address', 'recess', 'wwwgutenbergorgdonate', 'leaped', 'representative', 'bisulphate', 'prankupon', 'source', 'telegram', 'details', 'yawning', 'feathers', 'writings', 'hands', 'hysterical', 'excitable', 'unlink', 'brown', 'director', 'caltrops', 'differently', 'talking', 'disputatious', 'bears', 'fabrication', 'didnt', 'another', 'clearly', 'fought', 'critical', 'traced', 'ill', 'rattling', 'assurance', 'comely', 'intruding', 'untamed', 'passion', 'rang', 'bolted', 'curled', 'misjudged', 'opal', 'founder', 'utterly', 'campbed', 'risks', 'medium', 'dates', 'tired', 'archie', 'cooperation', 'corporation', 'balustraded', 'provision', 'limping', 'mission', 'turners', 'redheaded', 'mana', 'reconstruction', 'tassel', 'jollification', 'fowls', 'holmesi', 'protestation', 'infernal', 'attained', 'wherever', 'metropolitan', 'spellbound', 'label', 'underground', 'crab', 'snigger', 'fowler', 'company', 'assault', 'hours', 'pet', 'grown', 'make', 'possibleat', 'severn', 'guilt', 'westaway', 'crack', 'diversity', 'machine', 'governesses', 'arabian', 'ash', 'heels', 'gasped', 'swish', 'vainly', 'wax', 'undoing', 'preceded', 'definitely', 'burrowing', 'stormy', 'illness', 'agony', 'landingstages', 'air', 'destroy', 'hinders', 'coarse', 'by', 'thin', 'grime', 'suitor', 'fairbanks', 'splash', 'wagons', 'whined', 'event', 'dreadful', 'chivalrous', 'purchasing', 'electricblue', 'laurel', 'winking', 'undersecretary', 'confide', 'intellectual', 'forwarded', 'working', 'shy', 'diamond', 'admiration', 'palpitating', 'escapade', 'credit', 'up', 'selfevident', 'gems', 'invisible'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(words)\n",
+    "hello_encodded = word2tensor(\"junior\")\n",
+    "output, hidden = rnn(hello_encodded, hidden)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mps:0\n"
+     ]
+    }
+   ],
+   "source": [
+    "torch.set_default_device('mps')\n",
+    "a = torch.tensor([1, 2, 3, 4, 5])\n",
+    "print(a.device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor([[0., 0., 0., 0., 0.]], requires_grad=True)\n",
-      "tensor([[-1.6094, -1.6094, -1.6094, -1.6094, -1.6094]],\n",
-      "       grad_fn=<LogSoftmaxBackward0>)\n",
-      "tensor(-8.0472, grad_fn=<SumBackward0>)\n",
-      "tensor([2])\n",
-      "tensor([[-1.6094, -1.6094, -1.6094, -1.6094, -1.6094]],\n",
-      "       grad_fn=<LogSoftmaxBackward0>)\n",
-      "tensor(1.6094, grad_fn=<NllLossBackward0>)\n",
-      "tensor([[[-1.1418,  0.4812,  0.1088,  0.0671,  0.2627]]])\n",
-      "[0 1 2 3 4]\n",
-      "3\n"
+      "cpu\n"
      ]
     }
    ],
    "source": [
-    "m = nn.LogSoftmax(dim=1)\n",
-    "loss = nn.NLLLoss()\n",
-    "# input is of size N x C = 3 x 5\n",
-    "input = torch.zeros(1, 5, requires_grad=True)\n",
-    "print(input)\n",
-    "input = m(input)\n",
-    "print(input)\n",
-    "print(input.sum())\n",
-    "# each element in target has to have 0 <= value < C\n",
-    "target = torch.tensor([2])\n",
-    "print(target)\n",
-    "print(input)\n",
-    "output = loss(input, target)\n",
-    "output.backward()\n",
-    "print(output)\n",
-    "\n",
-    "sample = torch.randn(1, 1, 5)\n",
-    "print(sample)\n",
-    "# Print non-zero indices"
+    "b = torch.tensor([1, 2, 3, 4, 5], device='cpu')\n",
+    "print(b.device)"
    ]
   },
   {