class_docstr: In PyText DocNN models we need a way to access word embedding 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 `PyTextInterpretableEmbedding`
  layer which passes precomputed embedding vectors to lower layers.
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docstr: The forward pass of embedding layer. This can be for the text or any
  type of embedding.
  
  Args
  
     input: Input embeddings tensor
  
  Return
  
     output: Output tensor is the same as input. It passes through
             the embedding tensors to lower layers without any
             modifications
docstr_tok: ["The","forward","pass","of","embedding","layer",".","This","can","be","for","the","text","or","any","type","of","embedding",".","Args","input",":","Input","embeddings","tensor","Return","output",":","Output","tensor","is","the","same","as","input",".","It","passes","through","the","embedding","tensors","to","lower","layers","without","any","modifications"]
code: def forward(self, input):
      r"""
      The forward pass of embedding layer. This can be for the text or any
      type of embedding.
  
      Args
  
         input: Input embeddings tensor
  
      Return
  
         output: Output tensor is the same as input. It passes through
                 the embedding tensors to lower layers without any
                 modifications
      """
      return input
  
imports: from collections import defaultdict
  import torch
  from pytext.models.embeddings.dict_embedding import DictEmbedding
  from pytext.models.embeddings.word_embedding import WordEmbedding
  from pytext.models.model import EmbeddingBase, EmbeddingList
label_desc: Use image node_id 2 for calling the PyTextInterpretableEmbedding obj's underlying member method code with example usage: obj.forward(input) and returns: input