docstr: Inference function.
  
  Returns:
      output_heatmap (np.ndarray): Output heatmaps.
  
  Args:
      x (torch.Tensor[N,K,H,W]): Input features.
      flip_pairs (None | list[tuple]):
          Pairs of keypoints which are mirrored.
docstr_tok: ["Inference","function",".","Returns",":","output_heatmap","(","np.ndarray",")",":","Output","heatmaps",".","Args",":","x","(","torch.Tensor","[","N",",","K",",","H",",","W","]",")",":","Input","features",".","flip_pairs","(","None","|","list","[","tuple","]",")",":","Pairs","of","keypoints","which","are","mirrored","."]
code: def inference_model(self, x, flip_pairs=None):
      """Inference function.
  
      Returns:
          output_heatmap (np.ndarray): Output heatmaps.
  
      Args:
          x (torch.Tensor[N,K,H,W]): Input features.
          flip_pairs (None | list[tuple]):
              Pairs of keypoints which are mirrored.
      """
      output = self.forward(x)
  
      if flip_pairs is not None:
          output_heatmap = flip_back(
              output, self.flip_pairs, target_type=self.target_type
          )
          # feature is not aligned, shift flipped heatmap for higher accuracy
          if self.shift_heatmap:
              output_heatmap[:, :, :, 1:] = output_heatmap[:, :, :, :-1]
      else:
          output_heatmap = output
      return output_heatmap
  
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 6 for calling the TopdownHeatmapSimpleHead obj's underlying member method code with example usage: obj.inference_model(x, flip_pairs) and returns: output_heatmap