code: def forward(self, sample_list: Dict[str, Tensor]):
      if self._is_direct_features_input:
          if "input_modal" in sample_list:
              input_modal = sample_list["input_modal"]
          else:
              input_modal = sample_list["image_feature_0"]
      else:
          input_modal = sample_list["image"]
  
      modal_start_token: Optional[Tensor] = None
      if self.use_modal_start_token:
          modal_start_token = (
              sample_list["input_ids"][:, 0].clone().detach()
          )
  
      modal_end_token: Optional[Tensor] = None
      if self.use_modal_end_token:
          modal_end_token = self.extract_modal_end_token(sample_list)
  
      if "modal_token_type_ids" in sample_list:
          modal_token_type_ids = sample_list["modal_token_type_ids"]
      else:
          token_value = 0
          segment_ids = sample_list["segment_ids"]
          max_id = segment_ids.max()
          min_id = segment_ids.min()
          # Case of only one segment
          if max_id == min_id:
              # If max_id is greater than 0, that means text is at 0 segment
              # which means modal will be at 1
              # In other case, it will be zero, which it already is
              # NOTE: We compare with tensor here due to TorchScript compliance
              if max_id == torch.tensor(0, dtype=max_id.dtype):
                  token_value = 1
          else:
              max_segment = self.num_max_segment - 1
              # If max id is not equal to max_segment, it means
              # text segments start from 0 which means modal will
              # be last, otherwise, it is 0, which it already is
              if max_id != torch.tensor(
                  max_segment, dtype=max_id.dtype
              ):
                  token_value = max_segment
          modal_token_type_ids = torch.full(
              (input_modal.size(0), 1),
              fill_value=token_value,
              dtype=torch.long,
              device=input_modal.device,
          )
  
      # In case of XRAY, there might be only two dims
      if input_modal.dim() == 2:
          input_modal = input_modal.unsqueeze(dim=1)
  
      # See details of inputs at
      # https://github.com/huggingface/transformers/blob/1789c7/src/transformers/modeling_mmbt.py#L101 # noqa
      output = self.mmbt(
          input_modal,
          input_ids=sample_list["input_ids"],
          modal_start_tokens=modal_start_token,
          modal_end_tokens=modal_end_token,
          attention_mask=sample_list["input_mask"],
          token_type_ids=sample_list["segment_ids"],
          modal_token_type_ids=modal_token_type_ids,
          position_ids=None,
          modal_position_ids=None,
          head_mask=None,
          inputs_embeds=None,
          encoder_hidden_states=None,
          encoder_attention_mask=None,
      )
  
      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 4 for calling the MMBTBase obj's underlying member method code with example usage: obj.forward(sample_list) and returns: output