code: def test_training_gradient_checkpointing(self):
      if not self.model_tester.is_training:
          return
  
      (
          config,
          input_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
  
      model = TrajectoryTransformerModel(config)
      model.gradient_checkpointing_enable()
      model.to(torch_device)
      model.train()
      loss = model(
          trajectories=input_dict["trajectories"],
          attention_mask=input_dict["attention_mask"],
          targets=input_dict["targets"],
          output_hidden_states=True,
          output_attentions=True,
          use_cache=False,
          return_dict=True,
      ).loss
      loss.backward()
  
imports: import inspect
  import unittest
  import numpy
  from transformers import TrajectoryTransformerConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, random_attention_mask
label_desc: Use image node_id 9 for calling the TrajectoryTransformerModelTest obj's underlying member method code with example usage: obj.test_training_gradient_checkpointing() without return types