code: def arrange_model_inputs(self, tensor_dict):
      (
          doc_tokens,
          doc_seq_len,
          doc_mask,
          ques_tokens,
          ques_seq_len,
          ques_mask,
          *ignore,
      ) = tensor_dict["squad_input"]
      return (
          doc_tokens,
          doc_seq_len,
          doc_mask,
          ques_tokens,
          ques_seq_len,
          ques_mask,
      )
  
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 4 for calling the DrQAModel obj's underlying member method code with example usage: obj.arrange_model_inputs(tensor_dict) and returns: doc_tokens,  doc_seq_len,  doc_mask,  ques_tokens,  ques_seq_len,  ques_mask