code: def arrange_model_inputs(self, tensor_dict):
      model_inputs = (
          tensor_dict["right_tokens"],
          tensor_dict["left_tokens"],
          tensor_dict["right_dense"],
          tensor_dict["left_dense"],
      )
      return model_inputs
  
imports: from typing import Dict, List, Optional, Tuple
  import torch
  from pytext.common.constants import Stage, TORCH_VERSION
  from pytext.config import ConfigBase
  from pytext.data.roberta_tensorizer import RoBERTaTensorizer
  from pytext.data.tensorizers import FloatListTensorizer, NumericLabelTensorizer, Tensorizer
  from pytext.models.decoders.mlp_decoder_two_tower import MLPDecoderTwoTower
  from pytext.models.model import BaseModel
  from pytext.models.module import create_module
  from pytext.models.output_layers.doc_regression_output_layer import RegressionOutputLayer
  from pytext.models.roberta import RoBERTaEncoder, RoBERTaEncoderBase
  from pytext.torchscript.module import ScriptPyTextTwoTowerModuleWithDense
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 4 for calling the TwoTowerRegressionModel obj's underlying member method code with example usage: obj.arrange_model_inputs(tensor_dict) and returns: model_inputs