code: def flatten_for_bert(self, sample_list, **kwargs):
      to_be_flattened = [
          "input_ids",
          "token_type_ids",
          "input_mask",
          "image_mask",
          "masked_lm_labels",
          # "position_embeddings_visual",
          # "visual_embeddings_type",
      ]
      to_be_flattened_dim = [
          "visual_embeddings"
      ]  # "image_text_alignment",
  
      # We want to convert everything into: batch x sequence_length x (dim).
      flattened = self.flatten(
          sample_list, to_be_flattened, to_be_flattened_dim
      )
      return flattened
  
imports: import logging
  import math
  import os
  import torch
  from mmf.common.registry import registry
  from mmf.models import BaseModel
  from mmf.modules.embeddings import BertVisioLinguisticEmbeddings
  from mmf.utils.configuration import get_mmf_cache_dir
  from mmf.utils.file_io import PathManager
  from mmf.utils.modeling import get_optimizer_parameters_for_bert
  from mmf.utils.transform import transform_to_batch_sequence, transform_to_batch_sequence_dim
  from omegaconf import OmegaConf
  from torch import nn
label_desc: Use image node_id 5 for calling the VisualBERTModule obj's underlying member method code with example usage: obj.flatten_for_bert(sample_list) and returns: flattened