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
      self.num_labels = self.config.num_labels
      self.output_hidden_states = (
          self.encoder_config.output_hidden_states
      )
      self.output_attentions = self.encoder_config.output_attentions
      self.fused_feature_only = self.config.get(
          "fused_feature_only", False
      )
  
      self.dropout = nn.Dropout(self.encoder_config.hidden_dropout_prob)
      self.classifier = nn.Sequential(
          BertPredictionHeadTransform(self.encoder_config),
          nn.Linear(
              self.encoder_config.hidden_size, self.config.num_labels
          ),
      )
  
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 MMBTForClassification object from inherited base classes: nn with example: obj = MMBTForClassification(config)