import torch
import torch.nn as nn

# Define an enhanced neural network model with more layers
class ComplexModel(nn.Module):
    def __init__(self):
        super(ComplexModel, self).__init__()
        # First convolutional layer: input channels=3, output channels=16
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(16)
        
        # Second convolutional layer: input channels=16, output channels=32
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(32)
        
        # Apply max pooling to reduce spatial dimensions by a factor of 2
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        
        # Third convolutional layer: input channels=32, output channels=64
        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(64)
        
        # Fully connected layers:
        # For input tensors of shape (batch_size, 3, 4, 4):
        # - conv1 and conv2 keep spatial dimensions at 4x4.
        # - After conv2, we apply pooling, reducing 4x4 -> 2x2.
        # - conv3 keeps 2x2 spatial dimensions.
        # Thus, the flattened feature size is 64 * 2 * 2 = 256.
        self.fc1 = nn.Linear(64 * 2 * 2, 128)
        self.fc2 = nn.Linear(128, 10)  # For example, output layer with 10 classes

    def forward(self, x):
        # First conv layer with batch normalization and ReLU activation
        x = self.conv1(x)
        x = self.bn1(x)
        x = torch.relu(x)
        
        # Second conv layer with batch normalization and ReLU activation
        x = self.conv2(x)
        x = self.bn2(x)
        x = torch.relu(x)
        
        # Pooling to reduce spatial dimensions
        x = self.pool(x)
        
        # Third conv layer with batch normalization and ReLU activation
        x = self.conv3(x)
        x = self.bn3(x)
        x = torch.relu(x)
        
        # Flatten the tensor for the fully connected layers
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = torch.relu(x)
        x = self.fc2(x)
        return x

# Example of creating input tensors (each with shape: batch_size=2, channels=3, height=4, width=4)
tensor1 = torch.rand(2, 3, 4, 4)
tensor2 = torch.rand(2, 3, 4, 4)
tensor3 = torch.rand(2, 3, 4, 4)

# Adding the tensors element-wise to form the input tensor
input_tensor = tensor1 + tensor2 + tensor3

# Initialize the enhanced model
model = ComplexModel()

# Forward pass through the model
output = model(input_tensor)

print("Output shape:", output.shape)
print("Output:", output)