code: def _enhance_sample_list_for_pretraining(self, sample_list):
      bs = sample_list["input_ids"].size(0)
      sentence_len = sample_list["input_ids"].size(1)
  
      is_correct = torch.ones((bs,), dtype=torch.long)
      lm_label_ids = torch.zeros((bs, sentence_len), dtype=torch.long)
      input_ids_masked = sample_list["input_ids"]
      num_feat = sample_list["image_feat"].size(1)
      cls_dim = 1601
      image_info = {"cls_prob": torch.rand((bs, num_feat, cls_dim))}
      sample_list.add_field("is_correct", is_correct)
      sample_list.add_field("task", "mlm")
      sample_list.add_field("lm_label_ids", lm_label_ids)
      sample_list.add_field("input_ids_masked", input_ids_masked)
      sample_list.add_field("image_info_0", image_info)
      sample_list.to(get_current_device())
  
imports: import gc
  import unittest
  import tests.test_utils
  import torch
  from mmf.common.sample import SampleList
  from mmf.models.uniter import UNITERForClassification, UNITERForPretraining, UNITERImageEmbeddings, UNITERModelBase
  from mmf.utils.build import build_model
  from mmf.utils.configuration import Configuration
  from mmf.utils.env import setup_imports, teardown_imports
  from mmf.utils.general import get_current_device
  from omegaconf import OmegaConf
label_desc: Use image node_id 3 for calling the TestUniterWithHeads obj's underlying member method code with example usage: obj._enhance_sample_list_for_pretraining(sample_list) without return types