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
  
  Attributes:
      image_sizes (list[tuple[int, int]]): each tuple is (h, w)
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","Attributes",":","image_sizes","(","list","[","tuple","[","int",",","int","]","]",")",":","each","tuple","is","(","h",",","w",")"]
code: def to(self, *args: Any, **kwargs: Any) -> "ImageList":
      cast_tensor = self.tensor.to(*args, **kwargs)
      return ImageList(cast_tensor, self.image_sizes)
  
file_path: gluon-cv/gluoncv/torch/data/structures/image_list.py
imports: from __future__ import division
  from typing import Any, List, Sequence, Tuple, Union
  import torch
  from torch.nn import functional
label_desc: Use image node_id 4 for calling the ImageList obj's underlying member method code with example usage: obj.to() and returns: ImageList