def __call__(self, in_channels: int, out_channels: int) -> nn.Sequential:
        """Create and return the affine coupling subnet.

        The subnet consists of two convolutional layers with a ReLU activation in
        between. The intermediate channel dimension is determined by
        ``subnet_channels_ratio``.

        Args:
            in_channels (int): Number of input channels to the subnet.
            out_channels (int): Number of output channels from the subnet.

        Returns:
            nn.Sequential: Sequential container of the subnet layers including:
                - Conv2d layer mapping input to intermediate channels
                - ReLU activation
                - Conv2d layer mapping intermediate to output channels
        """
        mid_channels = int(in_channels * self.subnet_channels_ratio)
        return nn.Sequential(
            nn.Conv2d(in_channels, mid_channels, self.kernel_size, padding="same"),
            nn.ReLU(),
            nn.Conv2d(mid_channels, out_channels, self.kernel_size, padding="same"),
        )
Create and return the affine coupling subnet.

        The subnet consists of two convolutional layers with a ReLU activation in
        between. The intermediate channel dimension is determined by
        ``subnet_channels_ratio``.

        Args:
            in_channels (int): Number of input channels to the subnet.
            out_channels (int): Number of output channels from the subnet.

        Returns:
            nn.Sequential: Sequential container of the subnet layers including:
                - Conv2d layer mapping input to intermediate channels
                - ReLU activation
                - Conv2d layer mapping intermediate to output channels
        
src/anomalib/models/image/uflow/torch_model.py