code: def from_config(
      cls, config: Config, tensorizers: Dict[str, Tensorizer]
  ):
      # Although the RNN params are configurable, for DrQA we want to set
      # the following parameters for all cases.
      config.ques_rnn.dropout = config.dropout
      config.doc_rnn.dropout = config.dropout
  
      embedding = cls.create_embedding(config, tensorizers)
      ques_aligned_doc_attn = SequenceAlignedAttention(
          embedding.embedding_dim
      )
      ques_rnn = create_module(
          config.ques_rnn, input_size=embedding.embedding_dim
      )
      doc_rnn = create_module(
          config.doc_rnn, input_size=embedding.embedding_dim * 2
      )
      ques_self_attn = DotProductSelfAttention(
          ques_rnn.representation_dim
      )
      start_attn = MultiplicativeAttention(
          doc_rnn.representation_dim,
          ques_rnn.representation_dim,
          normalize=False,
      )
      end_attn = MultiplicativeAttention(
          doc_rnn.representation_dim,
          ques_rnn.representation_dim,
          normalize=False,
      )
      doc_rep_pool = SelfAttention(
          SelfAttention.Config(dropout=config.dropout),
          n_input=doc_rnn.representation_dim,
      )
      has_answer_labels = ["False", "True"]
      tensorizers["has_answer"].vocab = Vocabulary(has_answer_labels)
      has_ans_decoder = MLPDecoder(
          config=MLPDecoder.Config(),
          in_dim=doc_rnn.representation_dim,
          out_dim=len(has_answer_labels),
      )
      output_layer = create_module(
          config.output_layer,
          labels=has_answer_labels,
          is_kd=config.is_kd,
      )
      return cls(
          dropout=nn.Dropout(config.dropout),
          embedding=embedding,
          ques_rnn=ques_rnn,
          doc_rnn=doc_rnn,
          ques_self_attn=ques_self_attn,
          ques_aligned_doc_attn=ques_aligned_doc_attn,
          start_attn=start_attn,
          end_attn=end_attn,
          doc_rep_pool=doc_rep_pool,
          has_ans_decoder=has_ans_decoder,
          output_layer=output_layer,
          is_kd=config.is_kd,
      )
  
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 1 for calling the DrQAModel obj's underlying member method code with example usage: obj.from_config(cls, config, tensorizers) and returns: cls