code: def forward(self, *inputs):
      (
          tokens,
          pad_mask,
          segment_labels,
          _,
      ) = inputs  # See arrange_model_inputs()
      encoded_layers, cls_embed = self.encoder(inputs)
      pos_logits = self.decoder(encoded_layers[-1])
      if isinstance(pos_logits, (list, tuple)):
          pos_logits = pos_logits[0]
  
      has_answer_logits = (
          torch.zeros((pos_logits.size(0), 2))  # dummy tensor
          if self.output_layer.ignore_impossible
          else self.has_ans_decoder(cls_embed)
      )
      # Shape of logits is (batch_size, seq_len, 2)
      start_logits, end_logits = pos_logits.split(1, dim=-1)
  
      # Shape of start_logits and end_logits is (batch_size, seq_len, 1)
      # Hence, remove the last dimension and reduce them to the dimensions to
      # (batch_size, seq_len)
      start_logits = start_logits.squeeze(-1)
      end_logits = end_logits.squeeze(-1)
  
      return (
          start_logits,
          end_logits,
          has_answer_logits,
          pad_mask,
          segment_labels,
      )
  
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 5 for calling the BertSquadQAModel obj's underlying member method code with example usage: obj.forward() and returns: start_logits,  end_logits,  has_answer_logits,  pad_mask,  segment_labels