class_docstr: Helper class to merge final image on GPU. This generally faster than moving individual tiles to CPU.
docstr: Accumulates single element
  :param sample: Predicted image of shape [C,H,W]
  :param crop_coords: Corresponding tile crops w.r.t to original image
docstr_tok: ["Accumulates","single","element",":","param","sample",":","Predicted","image","of","shape","[","C",",","H",",","W","]",":","param","crop_coords",":","Corresponding","tile","crops","w.r.t","to","original","image"]
code: def accumulate_single(self, tile: torch.Tensor, coords):
      """
      Accumulates single element
      :param sample: Predicted image of shape [C,H,W]
      :param crop_coords: Corresponding tile crops w.r.t to original image
      """
      tile = tile.to(device=self.image.device)
      x, y, tile_width, tile_height = coords
      self.image[:, y : y + tile_height, x : x + tile_width] += (
          tile * self.weight
      )
      self.norm_mask[
          :, y : y + tile_height, x : x + tile_width
      ] += self.weight
  
imports: import dataclasses
  import math
  from typing import List, Iterable, Tuple, Union, Sequence
  import cv2
  import numpy
  import torch
label_desc: Use image node_id 2 for calling the TileMerger obj's underlying member method code with example usage: obj.accumulate_single(tile, coords) without return types