code: def add_post_flatten_params(
      self, sample_list: Dict[str, Tensor]
  ) -> Dict[str, Tensor]:
      sample_list["visual_embeddings_type"] = torch.zeros_like(
          sample_list["image_mask"]
      )
      attention_mask = torch.cat(
          (sample_list["input_mask"], sample_list["image_mask"]), dim=-1
      )
      sample_list["attention_mask"] = attention_mask
  
      if self.training_head_type == "pretraining":
          assert sample_list["masked_lm_labels"].size(
              -1
          ) == sample_list["input_mask"].size(-1)
          new_lm_labels = torch.ones_like(attention_mask) * -1
          size_masked_lm_labels = sample_list["masked_lm_labels"].size()
          assert len(size_masked_lm_labels) == 2
          new_lm_labels[
              : size_masked_lm_labels[0], : size_masked_lm_labels[1]
          ] = sample_list["masked_lm_labels"]
          sample_list["masked_lm_labels"] = new_lm_labels
  
      return sample_list
  
imports: import os
  from copy import deepcopy
  from typing import Dict, List, Optional, Tuple
  import torch
  from mmf.common.registry import registry
  from mmf.models import BaseModel
  from mmf.modules.embeddings import BertVisioLinguisticEmbeddings
  from mmf.modules.hf_layers import BertEncoderJit, BertLayerJit
  from mmf.utils.configuration import get_mmf_cache_dir
  from mmf.utils.modeling import get_optimizer_parameters_for_bert
  from mmf.utils.torchscript import getattr_torchscriptable
  from mmf.utils.transform import transform_to_batch_sequence, transform_to_batch_sequence_dim
  from omegaconf import OmegaConf
  from torch import nn, Tensor
label_desc: Use image node_id 5 for calling the VisualBERT obj's underlying member method code with example usage: obj.add_post_flatten_params(sample_list) and returns: sample_list