code: def create_embedding(
      cls, model_config: Config, tensorizers: Dict[str, Tensorizer]
  ):
      squad_tensorizer = tensorizers["squad_input"]
  
      # Initialize the embedding module.
      embedding_module = create_module(
          model_config.embedding, None, squad_tensorizer
      )
  
      # Set ques and doc tensorizer vocab to squad_tensorizer.vocab.
      squad_tensorizer.ques_tensorizer.vocab = squad_tensorizer.vocab
      squad_tensorizer.doc_tensorizer.vocab = squad_tensorizer.vocab
  
      return embedding_module
  
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 2 for calling the DrQAModel obj's underlying member method code with example usage: obj.create_embedding(cls, model_config, tensorizers) and returns: embedding_module