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: 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)
      fine_segm = self.fine_segm.to(device)
      u = self.u.to(device)
      v = self.v.to(device)
      return DensePoseChartPredictorOutput(
          coarse_segm=coarse_segm, fine_segm=fine_segm, u=u, v=v
      )
  
imports: from dataclasses import dataclass
  from typing import Union
  import torch
label_desc: Use image node_id 3 for calling the DensePoseChartPredictorOutput obj's underlying member method code with example usage: obj.to(device) and returns: DensePoseChartPredictorOutput