code: def test_cgan():
      """Test fitting a conditional GAN."""
  
      gan = ExampleGANModel(learning_rate=0.01)
      data = generate_data(gan, 500, 100)
      gan.fit_gan(data, generator_steps=0.5, checkpoint_interval=0)
  
      # See if it has done a plausible job of learning the distribution.
  
      means = 10 * np.random.random([1000, 1])
      values = gan.predict_gan_generator(conditional_inputs=[means])
      deltas = values - means
      assert abs(np.mean(deltas)) < 1.0
      assert np.std(deltas) > 1.0
      assert gan.get_global_step() == 500
  
file_path: deepchem/deepchem/models/torch_models/tests/test_gan.py
imports: import deepchem
  import numpy
  import pytest
  import tempfile
  from flaky import flaky
label_desc: Use image node_id 7 for calling a global function with example usage: test_cgan() without return types