def _transform_inputs(self, inputs):
        """Transform inputs for decoder.

        Args:
            inputs (list[Tensor] | Tensor): multi-level img features.

        Returns:
            Tensor: The transformed inputs
        """
        if not isinstance(inputs, list):
            return inputs

        if self.input_transform == 'resize_concat':
            inputs = [inputs[i] for i in self.in_index]
            upsampled_inputs = [
                resize(
                    input=x,
                    size=inputs[0].shape[2:],
                    mode='bilinear',
                    align_corners=self.align_corners) for x in inputs
            ]
            inputs = torch.cat(upsampled_inputs, dim=1)
        elif self.input_transform == 'multiple_select':
            inputs = [inputs[i] for i in self.in_index]
        else:
            inputs = inputs[self.in_index]

        return inputs
Transform inputs for decoder.

        Args:
            inputs (list[Tensor] | Tensor): multi-level img features.

        Returns:
            Tensor: The transformed inputs
        
mmpose/mmpose/models/necks/posewarper_neck.py