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: Get outputs for the selected instance(s)
  
  Args:
      item (int or slice or tensor): selected items
docstr_tok: ["Get","outputs","for","the","selected","instance","(","s",")","Args",":","item","(","int","or","slice","or","tensor",")",":","selected","items"]
code: def __getitem__(
      self, item: Union[int, slice, torch.BoolTensor]
  ) -> "DensePoseEmbeddingPredictorOutput":
      """
      Get outputs for the selected instance(s)
  
      Args:
          item (int or slice or tensor): selected items
      """
      if isinstance(item, int):
          return DensePoseEmbeddingPredictorOutput(
              coarse_segm=self.coarse_segm[item].unsqueeze(0),
              embedding=self.embedding[item].unsqueeze(0),
          )
      else:
          return DensePoseEmbeddingPredictorOutput(
              coarse_segm=self.coarse_segm[item],
              embedding=self.embedding[item],
          )
  
imports: from dataclasses import dataclass
  from typing import Union
  import torch
label_desc: Use image node_id 2 for calling the DensePoseEmbeddingPredictorOutput obj's underlying member method code with example usage: obj.__getitem__(item) and returns: DensePoseEmbeddingPredictorOutput, DensePoseEmbeddingPredictorOutput