code: def forward(
      self, sample_list: Dict[str, Tensor]
  ) -> Dict[str, Tensor]:
      # Sample preprocess
      orig_and_processed_sample_list = self.preprocess_sample(
          sample_list
      )
  
      orig_and_processed_sample_list["target_key"] = sample_list
      # Arrange masks in a list
      masks = []
      for modality in self.modality_keys:
          masks.append(
              orig_and_processed_sample_list["masks"][modality]
          )
  
      # Call transformer backend
      sequence_output, encoded_layers = self.backend(
          orig_and_processed_sample_list["input_ids"],
          orig_and_processed_sample_list["position_ids"],
          orig_and_processed_sample_list["segment_ids"],
          masks,
      )
  
      # Transformer Heads
      return self.postprocess_output(
          sequence_output,
          encoded_layers,
          orig_and_processed_sample_list,
      )
  
imports: import logging
  from dataclasses import dataclass, field
  from typing import Dict, List, Optional
  import torch
  from mmf.common.registry import registry
  from mmf.models.transformers.base import BaseTransformer, BaseTransformerBackendConfig, BaseTransformerHead, BaseTransformerModalityConfig
  from mmf.models.transformers.heads.mlp import MLP
  from mmf.modules.encoders import ResNet152ImageEncoder
  from mmf.utils.build import build_encoder
  from omegaconf import MISSING, OmegaConf
  from torch import nn, Tensor
label_desc: Use image node_id 14 for calling the MMFTransformer obj's underlying member method code with example usage: obj.forward(sample_list) and returns: self