code: def test_groupnorm_2d_different_groups():
      np.random.seed(0x2020)
      groups = [2, 1, 10]
      for i in groups:
          model = tf.keras.models.Sequential()
          norm = GroupNormalization(
              axis=1, groups=i, input_shape=(10, 3)
          )
          model.add(norm)
          # centered and variance are 5.0 and 10.0, respectively
          model.compile(loss="mse", optimizer="rmsprop")
          x = np.random.normal(loc=5.0, scale=10.0, size=(1000, 10, 3))
          model.fit(x, x, epochs=5, verbose=0)
          out = model.predict(x)
          out -= np.reshape(norm.beta.numpy(), (1, 10, 1))
          out /= np.reshape(norm.gamma.numpy(), (1, 10, 1))
  
          np.testing.assert_allclose(
              out.mean(axis=(0, 1), dtype=np.float32),
              (0.0, 0.0, 0.0),
              atol=1e-1,
          )
          np.testing.assert_allclose(
              out.std(axis=(0, 1), 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 18 for calling a global function with example usage: test_groupnorm_2d_different_groups() without return types