diff --git a/torchPractice.ipynb b/torchPractice.ipynb
index 5f4afae..2b76d6d 100644
--- a/torchPractice.ipynb
+++ b/torchPractice.ipynb
@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyMKIObSWw9zRBP98DJ8zV/c",
+      "authorship_tag": "ABX9TyOO6Xm7mvw5K+JRfZK6D4+F",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -23,7 +23,7 @@
         "colab_type": "text"
       },
       "source": [
-        "<a href=\"https://colab.research.google.com/github/krishnabhunia/googleColab_mycode/blob/test_review/torchPractice.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+        "<a href=\"https://colab.research.google.com/github/krishnabhunia/googleColab_mycode/blob/test_review_from_google_colab/torchPractice.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
     },
     {
@@ -198,6 +198,67 @@
           ]
         }
       ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a simple neural network model\n",
+        "class SimpleNet(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super(SimpleNet, self).__init__()\n",
+        "        self.fc1 = nn.Linear(5, 10)  # 5 input features, 10 hidden neurons\n",
+        "        self.fc2 = nn.Linear(10, 2)  # 10 hidden neurons, 2 output classes\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = torch.relu(self.fc1(x))\n",
+        "        x = self.fc2(x)\n",
+        "        return x\n",
+        "\n",
+        "# Instantiate the model, loss function, and optimizer\n",
+        "model = SimpleNet()\n",
+        "criterion = nn.CrossEntropyLoss()\n",
+        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
+        "\n",
+        "# Training loop\n",
+        "for epoch in range(10):\n",
+        "    for batch_X, batch_y in dataloader:\n",
+        "        # Forward pass\n",
+        "        outputs = model(batch_X)\n",
+        "        loss = criterion(outputs, batch_y)\n",
+        "\n",
+        "        # Backward pass and optimization\n",
+        "        optimizer.zero_grad()\n",
+        "        loss.backward()\n",
+        "        optimizer.step()\n",
+        "\n",
+        "    print(f'Epoch [{epoch+1}/10], Loss: {loss.item():.4f}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pXNFWZgR5_wX",
+        "outputId": "85870ab9-9cd0-4c45-d47d-e1c1e0e998b0"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch [1/10], Loss: 0.8287\n",
+            "Epoch [2/10], Loss: 0.5781\n",
+            "Epoch [3/10], Loss: 0.8722\n",
+            "Epoch [4/10], Loss: 0.6403\n",
+            "Epoch [5/10], Loss: 0.7962\n",
+            "Epoch [6/10], Loss: 0.7425\n",
+            "Epoch [7/10], Loss: 0.6679\n",
+            "Epoch [8/10], Loss: 0.7107\n",
+            "Epoch [9/10], Loss: 0.7298\n",
+            "Epoch [10/10], Loss: 0.5971\n"
+          ]
+        }
+      ]
     }
   ]
 }
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