class_docstr: A historical embedding storage module.
code: def __init__(
      self, num_embeddings: int, embedding_dim: int, device=None
  ):
      super().__init__()
  
      self.num_embeddings = num_embeddings
      self.embedding_dim = embedding_dim
  
      pin_memory = device is None or str(device) == "cpu"
      self.emb = torch.empty(
          num_embeddings,
          embedding_dim,
          device=device,
          pin_memory=pin_memory,
      )
  
      self._device = torch.device("cpu")
  
      self.reset_parameters()
  
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 1 to create a new History object from inherited base classes: torch.nn with example: obj = History(num_embeddings, embedding_dim, device)