def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass through the network.

        Args:
            x (torch.Tensor): Input tensor of shape ``(N, 3, H, W)``.

        Returns:
            torch.Tensor: Output feature maps of shape
                ``(N, out_channels, H', W')``, where ``H'`` and ``W'`` are
                determined by the network architecture and padding settings.
        """
        x = imagenet_norm_batch(x)
        x = F.relu(self.conv1(x))
        x = self.avgpool1(x)
        x = F.relu(self.conv2(x))
        x = self.avgpool2(x)
        x = F.relu(self.conv3(x))
        return self.conv4(x)
Forward pass through the network.

        Args:
            x (torch.Tensor): Input tensor of shape ``(N, 3, H, W)``.

        Returns:
            torch.Tensor: Output feature maps of shape
                ``(N, out_channels, H', W')``, where ``H'`` and ``W'`` are
                determined by the network architecture and padding settings.
        
src/anomalib/models/image/efficient_ad/torch_model.py