code: def _flip_segm_semantics_tensor(
      densepose_predictor_output: DensePoseChartPredictorOutput,
      dp_transform_data,
  ):
      if densepose_predictor_output.coarse_segm.shape[1] > 2:
          densepose_predictor_output.coarse_segm = (
              densepose_predictor_output.coarse_segm[
                  :, dp_transform_data.mask_label_symmetries, :, :
              ]
          )
      return densepose_predictor_output
  
imports: from dataclasses import fields
  import torch
  from densepose.structures import DensePoseChartPredictorOutput, DensePoseTransformData
label_desc: Use image node_id 3 for calling a global function with example usage: _flip_segm_semantics_tensor(densepose_predictor_output, dp_transform_data) and returns: densepose_predictor_output