def forward(self, x):
        """Forward pass.

        Args:
            x (tensor): input data (image)

        Returns:
            tensor: depth
        """
        if self.channels_last==True:
            print("self.channels_last = ", self.channels_last)
            x.contiguous(memory_format=torch.channels_last)


        layer_1 = self.pretrained.layer1(x)
        layer_2 = self.pretrained.layer2(layer_1)
        layer_3 = self.pretrained.layer3(layer_2)
        layer_4 = self.pretrained.layer4(layer_3)
        
        layer_1_rn = self.scratch.layer1_rn(layer_1)
        layer_2_rn = self.scratch.layer2_rn(layer_2)
        layer_3_rn = self.scratch.layer3_rn(layer_3)
        layer_4_rn = self.scratch.layer4_rn(layer_4)


        path_4 = self.scratch.refinenet4(layer_4_rn)
        path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
        path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
        
        out = self.scratch.output_conv(path_1)

        return torch.squeeze(out, dim=1)
Forward pass.

        Args:
            x (tensor): input data (image)

        Returns:
            tensor: depth
        
ldm/modules/midas/midas/midas_net_custom.py