code: def test_groupnorm_conv():
      # Check if Axis is working for CONV nets
      # Testing for 1 == LayerNorm, 5 == GroupNorm, -1 == InstanceNorm
      np.random.seed(0x2020)
      groups = [-1, 5, 1]
      for i in groups:
          model = tf.keras.models.Sequential()
          model.add(
              GroupNormalization(
                  axis=1, groups=i, input_shape=(20, 20, 3)
              )
          )
          model.add(tf.keras.layers.Conv2D(5, (1, 1), padding="same"))
          model.add(tf.keras.layers.Flatten())
          model.add(tf.keras.layers.Dense(1, activation="softmax"))
          model.compile(
              optimizer=tf.keras.optimizers.RMSprop(0.01), loss="mse"
          )
          x = np.random.randint(1000, size=(10, 20, 20, 3))
          y = np.random.randint(1000, size=(10, 1))
          model.fit(x=x, y=y, epochs=1)
          assert hasattr(model.layers[0], "gamma")
  
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 15 for calling a global function with example usage: test_groupnorm_conv() without return types