code: def _get_sample_list(self):
      bs = 8
      num_feats = 100
      max_sentence_len = 25
      img_dim = 2048
      vqa_cls_dim = 3129
      input_ids = torch.ones((bs, max_sentence_len), dtype=torch.long)
      input_mask = torch.ones((bs, max_sentence_len), dtype=torch.long)
      img_feat = torch.rand((bs, num_feats, img_dim))
  
      max_features = torch.ones((bs, num_feats)) * num_feats
      bbox = torch.randint(50, 200, (bs, num_feats, 4)).float()
      image_height = torch.randint(100, 300, (bs,))
      image_width = torch.randint(100, 300, (bs,))
      image_info = {
          "max_features": max_features,
          "bbox": bbox,
          "image_height": image_height,
          "image_width": image_width,
      }
      targets = torch.rand((bs, vqa_cls_dim))
      is_correct = torch.ones((bs,), dtype=torch.long)
  
      sample_list = SampleList()
      sample_list.add_field("input_ids", input_ids)
      sample_list.add_field("image_feature_0", img_feat)
      sample_list.add_field("input_mask", input_mask)
      sample_list.add_field("image_info_0", image_info)
      sample_list.add_field("targets", targets)
      sample_list.add_field("is_correct", is_correct)
      sample_list = sample_list.to(get_current_device())
      return sample_list
  
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 TestUniterModel obj's underlying member method code with example usage: obj._get_sample_list() and returns: sample_list