code: def __init__(
      self,
      encoder: nn.Module,
      decoder: nn.Module,
      has_ans_decoder: nn.Module,
      output_layer: nn.Module,
      stage: Stage = Stage.TRAIN,
      is_kd: bool = Config.is_kd,
  ) -> None:
      super().__init__(encoder, decoder, output_layer, stage)
      self.has_ans_decoder = has_ans_decoder
      self.module_list.append(has_ans_decoder)
      self.is_kd = is_kd
      log_class_usage(__class__)
  
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 2 to create a new BertSquadQAModel object from inherited base classes: NewBertModel with example: obj = BertSquadQAModel(encoder, decoder, has_ans_decoder, output_layer, stage, is_kd)