code: def forward(self, sample_list: Dict[str, Tensor]):
      module_output = self.bert(sample_list)
      pooled_output = module_output[1]
      output = {}
  
      if not torch.jit.is_scripting():
          if self.output_hidden_states or self.output_attentions:
              output["extras"] = module_output[2:]
      else:
          assert not (
              self.output_hidden_states or self.output_attentions
          ), "output_attentions or output_hidden_states not supported in script mode"
  
      pooled_output = self.dropout(pooled_output)
  
      if self.fused_feature_only:
          output["fused_feature"] = self.classifier[0](pooled_output)
          return output
  
      logits = self.classifier(pooled_output)
      reshaped_logits = logits.contiguous().view(-1, self.num_labels)
      output["scores"] = reshaped_logits
  
      return output
  
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 2 for calling the MMBTForClassification obj's underlying member method code with example usage: obj.forward(sample_list) and returns: output, output