code: def flatten(
      self,
      sample_list: Dict[str, Tensor],
      to_be_flattened: List[str],
      to_be_flattened_dim: List[str],
  ) -> Dict[str, Tensor]:
      for key in to_be_flattened:
          # Make sure these keys are present or otherwise set these keys to None
          sample_list[key] = transform_to_batch_sequence(
              sample_list[key]
          )
      for key in to_be_flattened_dim:
          sample_list[key] = transform_to_batch_sequence_dim(
              sample_list[key]
          )
      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 4 for calling the VisualBERT obj's underlying member method code with example usage: obj.flatten(sample_list, to_be_flattened, to_be_flattened_dim) and returns: sample_list