class_docstr: This class implements context-dependent word embeddings as described in
  https://arxiv.org/pdf/1607.00578.pdf
class_docstr_tok: ["This","class","implements","context-dependent","word","embeddings","as","described","in","https",":","\/\/arxiv.org\/pdf\/1607.00578.pdf"]
code: def __init__(self, embed_dim):
      super().__init__()
      self.nonlinear = NonlinearLayer(
          embed_dim, embed_dim, bias=True, activation_fn=nn.ReLU
      )
      self.linear = Linear(embed_dim, embed_dim, bias=True)
      self.sigmoid = torch.nn.Sigmoid()
  
imports: import abc
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqIncrementalDecoder, transformer
  from pytorch_translate import rnn_cell, utils, vocab_reduction
  from pytorch_translate.research.lexical_choice import lexical_translation
label_desc: Use image node_id 1 to create a new ContextEmbedding object from inherited base classes: nn with example: obj = ContextEmbedding(embed_dim)