code: def test_groupnorm_convnet():
      np.random.seed(0x2020)
      model = tf.keras.models.Sequential()
      norm = GroupNormalization(axis=1, input_shape=(3, 4, 4), groups=3)
      model.add(norm)
      model.compile(loss="mse", optimizer="sgd")
  
      # centered = 5.0, variance  = 10.0
      x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 3, 4, 4))
      model.fit(x, x, epochs=4, verbose=0)
      out = model.predict(x)
      out -= np.reshape(norm.beta.numpy(), (1, 3, 1, 1))
      out /= np.reshape(norm.gamma.numpy(), (1, 3, 1, 1))
  
      np.testing.assert_allclose(
          np.mean(out, axis=(0, 2, 3), dtype=np.float32),
          (0.0, 0.0, 0.0),
          atol=1e-1,
      )
      np.testing.assert_allclose(
          np.std(out, axis=(0, 2, 3), dtype=np.float32),
          (1.0, 1.0, 1.0),
          atol=1e-1,
      )
  
imports: import pytest
  import numpy
  import tensorflow
  from tensorflow_addons.layers.normalizations import FilterResponseNormalization
  from tensorflow_addons.layers.normalizations import GroupNormalization
  from tensorflow_addons.layers.normalizations import InstanceNormalization
label_desc: Use image node_id 19 for calling a global function with example usage: test_groupnorm_convnet() without return types