code: def test_eps_gards(dtype):
      set_random_seed()
      random_inputs = np.random.rand(10, 32, 32, 3).astype(np.float32)
      random_labels = np.random.randint(2, size=(10,)).astype(
          np.float32
      )
      input_layer = tf.keras.layers.Input(shape=(32, 32, 3))
      frn = FilterResponseNormalization(
          beta_initializer="ones",
          gamma_initializer="ones",
          learned_epsilon=True,
      )
      initial_eps_value = frn.eps_learned.numpy()[0]
      x = frn(input_layer)
      x = tf.keras.layers.Flatten()(x)
      out = tf.keras.layers.Dense(1, activation="sigmoid")(x)
      model = tf.keras.models.Model(input_layer, out)
      model.compile(loss="binary_crossentropy", optimizer="sgd")
      model.fit(random_inputs, random_labels, epochs=1)
      final_eps_value = frn.eps_learned.numpy()[0]
      assert initial_eps_value != final_eps_value
  
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 30 for calling a global function with example usage: test_eps_gards(dtype) without return types