code: def test_constrastive_learning(self):
      model = BridgeTowerForContrastiveLearning.from_pretrained(
          "BridgeTower/bridgetower-large-itm-mlm-itc"
      ).to(torch_device)
      model.eval()
      processor = BridgeTowerProcessor.from_pretrained(
          "BridgeTower/bridgetower-large-itm-mlm-itc"
      )
      image = prepare_img()
      text = "a bunch of cats laying on a tower."
      inputs = processor(
          image, text, padding=True, return_tensors="pt"
      ).to(torch_device)
      with torch.no_grad():
          outputs = model(
              **inputs, output_hidden_states=True, return_loss=True
          )
  
      # verify the logits
      expected_shape = torch.Size([1, 3, 512])
      self.assertEqual(outputs.logits.shape, expected_shape)
  
imports: import tempfile
  import unittest
  import numpy
  from transformers import BridgeTowerConfig, BridgeTowerTextConfig, BridgeTowerVisionConfig, is_torch_available, is_vision_available
  from transformers.testing_utils import require_torch, require_vision, slow, torch_device
  from transformers.utils import cached_property
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 4 for calling the BridgeTowerModelIntegrationTest obj's underlying member method code with example usage: obj.test_constrastive_learning() without return types