docstr: Parameters
  :context, (B, L, dim)
  :answers, (B, N, dim)
  :mask, (L, N)
  
  Returns
  :ques_emb, (L, dim)
docstr_tok: ["Parameters",":","context",",","(","B",",","L",",","dim",")",":","answers",",","(","B",",","N",",","dim",")",":","mask",",","(","L",",","N",")","Returns",":","ques_emb",",","(","L",",","dim",")"]
code: def forward(self, context, answers, out_answers, mask_answers=None):
      """
      Parameters
      :context, (B, L, dim)
      :answers, (B, N, dim)
      :mask, (L, N)
  
      Returns
      :ques_emb, (L, dim)
      """
      context_fc = torch.relu(self.linear_sim(context))
      questions_fc = torch.relu(self.linear_sim(answers))
  
      # shape: (B, L, N)
      attention = torch.matmul(
          context_fc, questions_fc.transpose(-1, -2)
      )
  
      if mask_answers is not None:
          attention = attention.masked_fill(
              ~mask_answers.unsqueeze(1).bool(), -Constants.INF
          )
  
      prob = torch.softmax(attention, dim=-1)
      # shape: (B, L, dim)
      emb = torch.matmul(prob, out_answers)
  
      return emb
  
file_path: graph4nlp/examples/pytorch/question_generation/fused_embedding_construction.py
imports: import torch
  import torch.nn
  from graph4nlp.pytorch.data.data import from_batch
  from graph4nlp.pytorch.modules.graph_embedding_initialization.embedding_construction import BertEmbedding, EmbeddingConstructionBase, RNNEmbedding, WordEmbedding
  from graph4nlp.pytorch.modules.utils import constants
  from graph4nlp.pytorch.modules.utils.generic_utils import dropout_fn
label_desc: Use image node_id 2 for calling the Context2AnswerAttention obj's underlying member method code with example usage: obj.forward(context, answers, out_answers, mask_answers) and returns: emb