code: def densepose_chart_predictor_output_hflip(
      densepose_predictor_output: DensePoseChartPredictorOutput,
      transform_data: DensePoseTransformData,
  ) -> DensePoseChartPredictorOutput:
      """
      Change  to take into account a Horizontal flip.
      """
      if len(densepose_predictor_output) > 0:
          PredictorOutput = type(densepose_predictor_output)
          output_dict = {}
  
          for field in fields(densepose_predictor_output):
              field_value = getattr(
                  densepose_predictor_output, field.name
              )
              # flip tensors
              if isinstance(field_value, torch.Tensor):
                  setattr(
                      densepose_predictor_output,
                      field.name,
                      torch.flip(field_value, [3]),
                  )
  
          densepose_predictor_output = _flip_iuv_semantics_tensor(
              densepose_predictor_output, transform_data
          )
          densepose_predictor_output = _flip_segm_semantics_tensor(
              densepose_predictor_output, transform_data
          )
  
          for field in fields(densepose_predictor_output):
              output_dict[field.name] = getattr(
                  densepose_predictor_output, field.name
              )
  
          return PredictorOutput(**output_dict)
      else:
          return densepose_predictor_output
  
imports: from dataclasses import fields
  import torch
  from densepose.structures import DensePoseChartPredictorOutput, DensePoseTransformData
label_desc: Use image node_id 1 for calling a global function with example usage: densepose_chart_predictor_output_hflip(densepose_predictor_output, transform_data) and returns: PredictorOutput, densepose_predictor_output