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

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

        Returns:
            torch.Tensor: Encoded features of shape ``(N, 64, H', W')``, where
                ``H'`` and ``W'`` are determined by the network architecture.
        """
        x = F.relu(self.enconv1(x))
        x = F.relu(self.enconv2(x))
        x = F.relu(self.enconv3(x))
        x = F.relu(self.enconv4(x))
        x = F.relu(self.enconv5(x))
        return self.enconv6(x)
Forward pass through the encoder network.

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

        Returns:
            torch.Tensor: Encoded features of shape ``(N, 64, H', W')``, where
                ``H'`` and ``W'`` are determined by the network architecture.
        
src/anomalib/models/image/efficient_ad/torch_model.py