code: def test_training(self):
      for model_class in self.all_training_supported_model_classes:
          config, inputs_dict = self._prepare_inputs_for_training(
              model_class
          )
          model = model_class(config)
          model.to(torch_device)
          model.train()
  
          loss = model(**inputs_dict).loss
          loss.backward()
  
          # verify the gradients of used layers' weight are not None
          for name, param in model.named_parameters():
              if self._is_layer_used(model_class, name):
                  self.assertIsNotNone(
                      param.grad,
                      f"Gradients should not be None - got {param.grad} for {name}",
                  )
  
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 5 for calling the BridgeTowerModelTrainingTest obj's underlying member method code with example usage: obj.test_training() without return types