class_docstr: A historical embedding storage module.
code: def _apply(self, fn):
      # Set the `_device` of the module without transfering `self.emb`.
      self._device = fn(torch.zeros(1)).device
      return self
  
file_path: DIG/dig/lsgraph/method/GraphFMOB/history.py
imports: from typing import Optional
  import torch
  from torch import Tensor
label_desc: Use image node_id 3 for calling the History obj's underlying member method code with example usage: obj._apply(fn) and returns: self