docstr: Transform inputs for decoder.
          
docstr_tok: ["Transform","inputs","for","decoder","."]
code: def _transform_inputs(self, inputs):
      """Transform inputs for decoder."""
      if not isinstance(inputs, list):
          if not isinstance(inputs, list):
              if self.upsample > 0:
                  inputs = resize(
                      input=F.relu(inputs),
                      scale_factor=self.upsample,
                      mode="bilinear",
                      align_corners=self.align_corners,
                  )
          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 = paddle.concat(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
  
file_path: PaddleDetection/ppdet/modeling/heads/vitpose_head.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from ppdet.modeling.keypoint_utils import resize, flip_back
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal
  from ppdet.modeling.layers import ConvTranspose2d, BatchNorm2d
label_desc: Use image node_id 4 for calling the TopdownHeatmapSimpleHead obj's underlying member method code with example usage: obj._transform_inputs(inputs) and returns: inputs, inputs