code: def arrange_model_inputs(self, tensor_dict):
      (
          tokens,
          pad_mask,
          segment_labels,
          positions,
          *ignore,
      ) = tensor_dict["squad_input"]
      return tokens, pad_mask, segment_labels, positions
  
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 3 for calling the BertSquadQAModel obj's underlying member method code with example usage: obj.arrange_model_inputs(tensor_dict) and returns: tokens,  pad_mask,  segment_labels,  positions