class_docstr: A loader to cache mini-batch outputs, e.g., obtained during
  :class:`NeighborLoader` iterations.
  
  Args:
      loader (torch.utils.data.DataLoader): The data loader.
      device (torch.device, optional): The device to load the data to.
          (default: :obj:`None`)
      transform (callable, optional): A function/transform that takes in
          a sampled mini-batch and returns a transformed version.
          (default: :obj:`None`)
class_docstr_tok: ["A","loader","to","cache","mini-batch","outputs",",","e.g.",",","obtained","during",":","class",":","`","NeighborLoader","`","iterations",".","Args",":","loader","(","torch.utils.data.DataLoader",")",":","The","data","loader",".","device","(","torch.device",",","optional",")",":","The","device","to","load","the","data","to",".","(","default",":",":","obj",":","`","None","`",")","transform","(","callable",",","optional",")",":","A","function\/transform","that","takes","in","a","sampled","mini-batch","and","returns","a","transformed","version",".","(","default",":",":","obj",":","`","None","`",")"]
code: def __repr__(self) -> str:
      return f"{self.__class__.__name__}({self.loader})"
  
file_path: pytorch_geometric/torch_geometric/loader/cache.py
imports: from collections.abc import Mapping
  from typing import Any, Callable, List, Optional, Sequence
  import torch
  from torch.utils.data import DataLoader
label_desc: Use image node_id 5 for calling the CachedLoader obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str