class_docstr: Predictor output that contains segmentation and inner coordinates predictions for predefined
  body parts:
   * coarse segmentation, a tensor of shape [N, K, Hout, Wout]
   * fine segmentation, a tensor of shape [N, C, Hout, Wout]
   * U coordinates, a tensor of shape [N, C, Hout, Wout]
   * V coordinates, a tensor of shape [N, C, Hout, Wout]
  where
   - N is the number of instances
   - K is the number of coarse segmentation channels (
       2 = foreground / background,
       15 = one of 14 body parts / background)
   - C is the number of fine segmentation channels (
       24 fine body parts / background)
   - Hout and Wout are height and width of predictions
docstr: Number of instances (N) in the output
docstr_tok: ["Number","of","instances","(","N",")","in","the","output"]
code: def __len__(self):
      """
      Number of instances (N) in the output
      """
      return self.coarse_segm.size(0)
  
imports: from dataclasses import dataclass
  from typing import Union
  import torch
label_desc: Use image node_id 1 for calling the DensePoseChartPredictorOutput obj's underlying member method code with example usage: obj.__len__() and returns: self