diff --git a/src/test_main.py b/src/test_main.py
new file mode 100644
index 0000000..5363417
--- /dev/null
+++ b/src/test_main.py
@@ -0,0 +1,53 @@
+import pytest
+from pytest_mock import MockerFixture
+import torch
+from torchvision import datasets, transforms
+from torch.utils.data import DataLoader
+from main import Net
+
+def test_data_loading_and_preprocessing(mocker: MockerFixture):
+    mock_mnist = mocker.patch.object(datasets, 'MNIST')
+    mock_dataloader = mocker.patch.object(DataLoader, '__init__', return_value=None)
+
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.5,), (0.5,))
+    ])
+
+    trainset = datasets.MNIST('.', download=True, train=True, transform=transform)
+    trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
+
+    mock_mnist.assert_called_once_with('.', download=True, train=True, transform=transform)
+    mock_dataloader.assert_called_once_with(trainset, batch_size=64, shuffle=True)
+
+    assert isinstance(trainset, datasets.MNIST)
+    assert isinstance(trainloader, DataLoader)
+
+def test_model_definition():
+    model = Net()
+
+    assert isinstance(model, Net)
+    assert isinstance(model.fc1, torch.nn.Linear)
+    assert isinstance(model.fc2, torch.nn.Linear)
+    assert isinstance(model.fc3, torch.nn.Linear)
+
+    input_data = torch.randn(64, 1, 28, 28)
+    output = model(input_data)
+
+    assert output.size() == (64, 10)
+    assert output.dtype == torch.float32
+
+def test_forward_method(mocker: MockerFixture):
+    mock_relu = mocker.patch('torch.nn.functional.relu')
+    mock_log_softmax = mocker.patch('torch.nn.functional.log_softmax')
+
+    model = Net()
+    input_data = torch.randn(64, 1, 28, 28)
+    output = model(input_data)
+
+    mock_relu.assert_any_call(model.fc1(input_data.view(-1, 28 * 28)))
+    mock_relu.assert_any_call(model.fc2(mock_relu.return_value))
+    mock_log_softmax.assert_called_once_with(model.fc3(mock_relu.return_value), dim=1)
+
+    assert output.size() == (64, 10)
+    assert output.dtype == torch.float32