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 forward(self, src):
      c = torch.mean(self.nonlinear(src), 1, True)
      return src * self.sigmoid(self.linear(c))
  
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 2 for calling the ContextEmbedding obj's underlying member method code with example usage: obj.forward(src) and returns: unknown