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.
docstr: Maps indices to corresponding embedding vectors. E.g. word embeddings
  
  Args:
  
      *input (Any, optional): This can be a tensor(s) of input indices or any
              other variable necessary to comput the embeddings. A typical
              example of input indices are word or token indices.
      **kwargs (Any, optional): Similar to `input` this can be any sequence
              of key-value arguments necessary to compute final embedding
              tensor.
  Returns:
  
      tensor:
      A tensor of word embeddings corresponding to the
      indices specified in the input
docstr_tok: ["Maps","indices","to","corresponding","embedding","vectors",".","E.g",".","word","embeddings","Args",":","*","input","(","Any",",","optional",")",":","This","can","be","a","tensor","(","s",")","of","input","indices","or","any","other","variable","necessary","to","comput","the","embeddings",".","A","typical","example","of","input","indices","are","word","or","token","indices",".","*","*","kwargs","(","Any",",","optional",")",":","Similar","to","`","input","`","this","can","be","any","sequence","of","key-value","arguments","necessary","to","compute","final","embedding","tensor",".","Returns",":","tensor",":","A","tensor","of","word","embeddings","corresponding","to","the","indices","specified","in","the","input"]
code: def indices_to_embeddings(self, *input, **kwargs):
      r"""
      Maps indices to corresponding embedding vectors. E.g. word embeddings
  
      Args:
  
          *input (Any, optional): This can be a tensor(s) of input indices or any
                  other variable necessary to comput the embeddings. A typical
                  example of input indices are word or token indices.
          **kwargs (Any, optional): Similar to `input` this can be any sequence
                  of key-value arguments necessary to compute final embedding
                  tensor.
      Returns:
  
          tensor:
          A tensor of word embeddings corresponding to the
          indices specified in the input
      """
      return self.embedding(*input, **kwargs)
  
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 3 for calling the InterpretableEmbeddingBase obj's underlying member method code with example usage: obj.indices_to_embeddings() and returns: self