code: def __init__(
      self,
      dropout: nn.Module,
      embedding: nn.Module,
      ques_rnn: nn.Module,
      doc_rnn: nn.Module,
      ques_self_attn: nn.Module,
      ques_aligned_doc_attn: nn.Module,
      start_attn: nn.Module,
      end_attn: nn.Module,
      doc_rep_pool: nn.Module,
      has_ans_decoder: nn.Module,
      output_layer: nn.Module,
      is_kd: bool = Config.is_kd,
  ) -> None:
      super().__init__()
      self.dropout = dropout
      self.embedding = embedding
      self.ques_rnn = ques_rnn
      self.doc_rnn = doc_rnn
      self.ques_self_attn = ques_self_attn
      self.ques_aligned_doc_attn = ques_aligned_doc_attn
      self.start_attn = start_attn
      self.end_attn = end_attn
      self.doc_rep_pool = doc_rep_pool
      self.has_ans_decoder = has_ans_decoder
      self.output_layer = output_layer
      self.ignore_impossible = output_layer.ignore_impossible
      self.module_list = [
          embedding,
          ques_rnn,
          doc_rnn,
          ques_self_attn,
          ques_aligned_doc_attn,
          start_attn,
          end_attn,
          has_ans_decoder,
      ]
      self.is_kd = is_kd
      log_class_usage(__class__)
  
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 3 to create a new DrQAModel object from inherited base classes: BaseModel with example: obj = DrQAModel(dropout, embedding, ques_rnn, doc_rnn, ques__attn, ques_aligned_doc_attn, start_attn, end_attn, doc_rep_pool, has_ans_decoder, output_layer, is_kd)