code: def test_constraints_layernorm_rnn():
      embedding_dim = 4
      k_constraint = keras.constraints.max_norm(0.01)
      r_constraint = keras.constraints.max_norm(0.01)
      b_constraint = keras.constraints.max_norm(0.01)
      g_constraint = keras.constraints.max_norm(0.01)
      layer = keras.layers.RNN(
          LayerNormSimpleRNNCell(
              units=5,
              kernel_constraint=k_constraint,
              recurrent_constraint=r_constraint,
              bias_constraint=b_constraint,
              gamma_constraint=g_constraint,
          ),
          input_shape=(None, embedding_dim),
          return_sequences=False,
      )
      layer.build((None, None, embedding_dim))
      assert layer.cell.kernel.constraint == k_constraint
      assert layer.cell.recurrent_kernel.constraint == r_constraint
      assert layer.cell.bias.constraint == b_constraint
      assert layer.cell.layernorm.gamma.constraint == g_constraint
  
imports: import numpy
  import tensorflow.keras
  from tensorflow_addons.rnn import LayerNormSimpleRNNCell
label_desc: Use image node_id 1 for calling a global function with example usage: test_constraints_layernorm_rnn() without return types