code: def mock_vinvl_input_tensors(
      cls, bs=8, num_feats=70, max_sentence_len=25, img_feature_dim=2054
  ):
      cls.input_ids = torch.ones(
          (bs, max_sentence_len), dtype=torch.long
      )
      cls.img_feats = torch.rand((bs, num_feats, img_feature_dim))
      cls.attention_mask = torch.ones(
          (bs, max_sentence_len + num_feats), dtype=torch.long
      )
      cls.token_type_ids = torch.zeros_like(cls.input_ids)
      cls.labels = torch.ones((bs, 1)).long()
  
      cls.lm_label_ids = -torch.ones_like(cls.input_ids).long()
      cls.contrastive_labels = torch.zeros((bs, 1)).long()
  
imports: import unittest
  import tests.test_utils
  import torch
  from mmf.common.sample import SampleList
  from mmf.models.vinvl import VinVLBase, VinVLForClassification, VinVLForPretraining
  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 1 for calling a global function with example usage: mock_vinvl_input_tensors(cls, bs, num_feats, max_sentence_len, img_feature_dim) without return types