import torch.nn as nn
import torch

class LeNNon(nn.Module):
    def __init__(self):
        """ Define a CNN architecture used for image classification.
            This class defines the LeNNon architecture as a PyTorch module
        """
        super(LeNNon, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(32 * 25 * 25, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        """
        Perform a forward pass through the LeNNon architecture.

        This method applies the convolutional layers, max pooling layers,
        and fully connected layers to the input tensor x.

        Parameters:
        -----------
        x (torch.Tensor): The input tensor.

        Returns:
        --------
        torch.Tensor: The output tensor.
        """
        x = self.pool(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 32 * 25 * 25)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x