code: def setUp(self):
      bs = 32
      num_feat = 100
      self.config = OmegaConf.create(
          {"img_dim": 1024, "hidden_size": 256, "pos_dim": 7}
      )
      self.img_feat = torch.rand((bs, num_feat, self.config["img_dim"]))
      self.img_pos_feat = torch.rand(
          (bs, num_feat, self.config["pos_dim"])
      )
      self.type_embeddings = torch.ones(
          (bs, num_feat, 1), dtype=torch.long
      )
  
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 1 for calling the TestUNITERImageEmbeddings obj's underlying member method code with example usage: obj.setUp() without return types