class_docstr: Since some embedding vectors, e.g. word are created and assigned in
  the embedding layers of Pytorch models we need a way to access
  those layers, generate the embeddings and subtract the baseline.
  To do so, we separate embedding layers from the model, compute the
  embeddings separately and do all operations needed outside of the model.
  The original embedding layer is being replaced by
  `InterpretableEmbeddingBase` layer which passes already
  precomputed embedding vectors to the layers below.
code: def __init__(self, embedding, full_name) -> None:
      Module.__init__(self)
      self.num_embeddings = getattr(embedding, "num_embeddings", None)
      self.embedding_dim = getattr(embedding, "embedding_dim", None)
  
      self.embedding = embedding
      self.full_name = full_name
  
file_path: captum/captum/attr/_models/base.py
imports: import warnings
  from functools import reduce
  import torch
  from torch.nn import Module
label_desc: Use image node_id 1 to create a new InterpretableEmbeddingBase object from inherited base classes: Module with example: obj = InterpretableEmbeddingBase(embedding, full_name)