code: def forward(
      self,
      doc_tokens: torch.Tensor,
      doc_seq_len: torch.Tensor,
      doc_mask: torch.Tensor,
      ques_tokens: torch.Tensor,
      ques_seq_len: torch.Tensor,
      ques_mask: torch.Tensor,
  ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
      # Embedding lookups.
      doc_embedded = self.dropout(self.embedding(doc_tokens))
      ques_embedded = self.dropout(self.embedding(ques_tokens))
  
      # Document encoding.
      doc_attn_weights = self.ques_aligned_doc_attn(
          doc_embedded, ques_embedded, ques_mask
      )
      ques_aligned_doc_embedded = doc_attn_weights.bmm(ques_embedded)
      doc_embedded = torch.cat(
          [doc_embedded, ques_aligned_doc_embedded], dim=2
      )
      doc_seq_vec = self.doc_rnn(doc_embedded, doc_mask)
  
      # Question encoding.
      ques_seq_vec = self.ques_rnn(ques_embedded, ques_mask)
      ques_attn_weights = self.ques_self_attn(ques_seq_vec, ques_mask)
      ques_vec = (
          ques_attn_weights.unsqueeze(1).bmm(ques_seq_vec).squeeze(1)
      )
  
      # Apply dropout to the ques and document representations.
      doc_seq_vec = self.dropout(doc_seq_vec)
      ques_vec = self.dropout(ques_vec)
  
      # Compute bilinear attention weights for each document token.
      # These attention weights serve as logits for detecting start and end.
      start_logits = self.start_attn(doc_seq_vec, ques_vec, doc_mask)
      end_logits = self.end_attn(doc_seq_vec, ques_vec, doc_mask)
  
      has_answer_logits = torch.zeros(
          ques_tokens.size(0),  # batch_size
          self.has_ans_decoder.out_dim,  # Number of classes
      )
      if (
          not self.ignore_impossible
      ):  # Compute whether document has an answer.
          doc_vec = self.doc_rep_pool(doc_seq_vec, doc_seq_len)
          has_answer_logits = F.relu(self.has_ans_decoder(doc_vec))
  
      # start_logits and end_logits: batch_size, max_seq_len
      # has_answer_logit: batch_size, 2
      return start_logits, end_logits, has_answer_logits
  
imports: from typing import Dict, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from pytext.data.squad_tensorizer import SquadTensorizer
  from pytext.data.tensorizers import LabelTensorizer, Tensorizer
  from pytext.data.utils import Vocabulary
  from pytext.models.decoders.mlp_decoder import MLPDecoder
  from pytext.models.embeddings import WordEmbedding
  from pytext.models.model import BaseModel
  from pytext.models.module import create_module
  from pytext.models.output_layers.squad_output_layer import SquadOutputLayer
  from pytext.models.representations.attention import DotProductSelfAttention, MultiplicativeAttention, SequenceAlignedAttention
  from pytext.models.representations.pooling import SelfAttention
  from pytext.models.representations.stacked_bidirectional_rnn import StackedBidirectionalRNN
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 6 for calling the DrQAModel obj's underlying member method code with example usage: obj.forward(doc_tokens, doc_seq_len, doc_mask, ques_tokens, ques_seq_len, ques_mask) and returns: start_logits,  end_logits,  has_answer_logits