code: def flatten(
      self, sample_list, to_be_flattened=None, to_be_flattened_dim=None
  ):
      if to_be_flattened is None:
          to_be_flattened = {}
      if to_be_flattened_dim is None:
          to_be_flattened_dim = {}
      for key in to_be_flattened:
          # Make sure these keys are present or otherwise set these keys to None
          sample_list[key] = getattr(sample_list, key, None)
          sample_list[key] = transform_to_batch_sequence(
              sample_list[key]
          )
      for key in to_be_flattened_dim:
          sample_list[key] = getattr(sample_list, key, None)
          sample_list[key] = transform_to_batch_sequence_dim(
              sample_list[key]
          )
  
      if sample_list.visual_embeddings_type is None:
          if sample_list.image_mask is not None:
              sample_list.visual_embeddings_type = torch.zeros_like(
                  sample_list.image_mask
              )
  
      if sample_list.image_mask is not None:
          attention_mask = torch.cat(
              (sample_list.input_mask, sample_list.image_mask), dim=-1
          )
          if sample_list.masked_lm_labels is not None:
              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
      else:
          attention_mask = sample_list.input_mask
  
      sample_list.attention_mask = attention_mask
  
      return sample_list
  
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 3 for calling the VisualBERTModule obj's underlying member method code with example usage: obj.flatten(sample_list, to_be_flattened, to_be_flattened_dim) and returns: sample_list