class_docstr: Predictor output that contains embedding and coarse segmentation data:
   * embedding: float tensor of size [N, D, H, W], contains estimated embeddings
   * coarse_segm: float tensor of size [N, K, H, W]
  Here D = MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE
       K = MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS
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 DensePoseEmbeddingPredictorOutput obj's underlying member method code with example usage: obj.__len__() and returns: self