code: def process_forward_inputs(self, batch):
      if self.model_name != "unilm":
          return super().process_forward_inputs(batch)
      inputs = {
          "input_ids": batch["source_ids"].to(self.device),
          "attention_mask": batch["source_mask"].to(self.device),
          "token_type_ids": batch["token_type_ids"].to(self.device),
          "masked_lm_labels": batch["masked_lm_labels"].to(self.device),
          "masked_pos": batch["masked_pos"].to(self.device),
          "masked_weights": batch["masked_weights"].to(self.device),
      }
      return inputs
  
file_path: TextBox/textbox/model/pretrained_models.py
imports: import os
  import copy
  import torch
  import torch.nn
  import warnings
  import inspect
  from .abstract_model import AbstractModel
  from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeq2SeqLM, EncoderDecoderModel
  from transformers.models.encoder_decoder.configuration_encoder_decoder import EncoderDecoderConfig
  from ..utils.argument_list import efficient_kwargs_dict
label_desc: Use image node_id 4 for calling the Pretrained_Models obj's underlying member method code with example usage: obj.process_forward_inputs(batch) and returns: inputs, super