class_docstr: Structure that holds a list of images (of possibly
  varying sizes) as a single tensor.
  This works by padding the images to the same size,
  and storing in a field the original sizes of each image
class_docstr_tok: ["Structure","that","holds","a","list","of","images","(","of","possibly","varying","sizes",")","as","a","single","tensor",".","This","works","by","padding","the","images","to","the","same","size",",","and","storing","in","a","field","the","original","sizes","of","each","image"]
docstr: Arguments:
      tensors (tensor) padding后的图像数据
      image_sizes (list[tuple[int, int]])  padding前的图像尺寸
docstr_tok: ["Arguments",":","tensors","(","tensor",")","padding\u540e\u7684\u56fe\u50cf\u6570\u636e","image_sizes","(","list","[","tuple","[","int",",","int","]","]",")","padding\u524d\u7684\u56fe\u50cf\u5c3a\u5bf8"]
code: def __init__(self, tensors, image_sizes):
      # type: (Tensor, List[Tuple[int, int]]) -> None
      """
      Arguments:
          tensors (tensor) padding后的图像数据
          image_sizes (list[tuple[int, int]])  padding前的图像尺寸
      """
      self.tensors = tensors
      self.image_sizes = image_sizes
  
imports: from typing import List, Tuple
  from torch import Tensor
label_desc: Use image node_id 1 to create a new ImageList object from inherited base classes: object with example: obj = ImageList(tensors, image_sizes)