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: Transfers all tensors to the given device
docstr_tok: ["Transfers","all","tensors","to","the","given","device"]
code: def to(self, device: torch.device):
      """
      Transfers all tensors to the given device
      """
      coarse_segm = self.coarse_segm.to(device)
      embedding = self.embedding.to(device)
      return DensePoseEmbeddingPredictorOutput(
          coarse_segm=coarse_segm, embedding=embedding
      )
  
imports: from dataclasses import dataclass
  from typing import Union
  import torch
label_desc: Use image node_id 3 for calling the DensePoseEmbeddingPredictorOutput obj's underlying member method code with example usage: obj.to(device) and returns: DensePoseEmbeddingPredictorOutput