code: def from_config(cls, config: Config, tensorizers):
      has_answer_labels = ["False", "True"]
      tensorizers["has_answer"].vocab = Vocabulary(has_answer_labels)
      vocab = tensorizers["squad_input"].vocab
  
      encoder = create_module(
          config.encoder,
          output_encoded_layers=True,
          padding_idx=vocab.get_pad_index(),
          vocab_size=vocab.__len__(),
      )
  
      pos_decoder = create_module(
          config.pos_decoder,
          in_dim=encoder.representation_dim,
          out_dim=2,
      )
      has_ans_decoder = create_module(
          config.has_ans_decoder,
          in_dim=encoder.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(
          encoder,
          pos_decoder,
          has_ans_decoder,
          output_layer,
          is_kd=config.is_kd,
      )
  
imports: from typing import Union
  import torch
  import torch.nn
  from pytext.common.constants import Stage
  from pytext.data.squad_for_bert_tensorizer import SquadForBERTTensorizer, SquadForRoBERTaTensorizer
  from pytext.data.tensorizers import LabelTensorizer
  from pytext.data.utils import Vocabulary
  from pytext.models.bert_classification_models import NewBertModel
  from pytext.models.decoders.mlp_decoder import MLPDecoder
  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.huggingface_bert_sentence_encoder import HuggingFaceBertSentenceEncoder
  from pytext.models.representations.huggingface_electra_sentence_encoder import HuggingFaceElectraSentenceEncoder
  from pytext.models.representations.transformer_sentence_encoder_base import TransformerSentenceEncoderBase
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 1 for calling the BertSquadQAModel obj's underlying member method code with example usage: obj.from_config(cls, config, tensorizers) and returns: cls