code: def test_mix_gan():
      """Test a GAN with multiple generators and discriminators."""
  
      gan = ExampleGANModel(
          n_generators=2, n_discriminators=2, learning_rate=0.01
      )
      data = generate_data(gan, 1000, 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])
      for i in range(2):
          values = gan.predict_gan_generator(
              conditional_inputs=[means], generator_index=i
          )
          deltas = values - means
          assert abs(np.mean(deltas)) < 1.0
          assert np.std(deltas) > 1.0
      assert gan.get_global_step() == 1000
  
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 9 for calling a global function with example usage: test_mix_gan() without return types