code: def __init__(self, config, *args, **kwargs):
      super().__init__()
      self.config = config
      self.bert = MMBTBase(config, *args, **kwargs)
      self.encoder_config = self.bert.encoder_config
  
      # TODO : Switch to AutoModelForPreTraining after transformers
      # package upgrade to 2.5
      pretraining_module = BertForPreTraining.from_pretrained(
          self.config.bert_model_name,
          config=self.encoder_config,
          cache_dir=os.path.join(
              get_mmf_cache_dir(), "distributed_{}".format(-1)
          ),
      )
  
      self.cls = deepcopy(pretraining_module.cls)
      self.loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
      self.tie_weights()
  
imports: import os
  from copy import deepcopy
  from dataclasses import dataclass
  from typing import Dict, Optional, Union
  import torch
  from mmf.common.registry import registry
  from mmf.models.base_model import BaseModel
  from mmf.models.interfaces.mmbt import MMBTGridHMInterface
  from mmf.modules.encoders import EncoderFactory, ImageEncoderFactory, ImageEncoderTypes, MultiModalEncoderBase, ResNet152ImageEncoder, TextEncoderFactory, TextEncoderTypes, TransformerEncoder
  from mmf.modules.hf_layers import replace_with_jit
  from mmf.utils.checkpoint import load_pretrained_model
  from mmf.utils.configuration import get_mmf_cache_dir
  from mmf.utils.modeling import get_optimizer_parameters_for_bert
  from omegaconf import DictConfig, II, OmegaConf
  from torch import nn, Tensor
label_desc: Use image node_id 1 to create a new MMBTForPreTraining object from inherited base classes: nn with example: obj = MMBTForPreTraining(config)