def METHOD_NAME():
    cell = ESNCell(
        units=3,
        connectivity=1,
        leaky=1,
        spectral_radius=0.9,
        use_norm2=False,
        use_bias=True,
        activation="tanh",
        kernel_initializer="glorot_uniform",
        recurrent_initializer="glorot_uniform",
        bias_initializer="glorot_uniform",
        name="esn_cell_3",
    )
    expected_config = {
        "name": "esn_cell_3",
        "trainable": True,
        "dtype": "float32",
        "units": 3,
        "connectivity": 1,
        "leaky": 1,
        "spectral_radius": 0.9,
        "use_norm2": False,
        "use_bias": True,
        "activation": tf.keras.activations.serialize(tf.keras.activations.get("tanh")),
        "kernel_initializer": tf.keras.initializers.serialize(
            tf.keras.initializers.get("glorot_uniform")
        ),
        "recurrent_initializer": tf.keras.initializers.serialize(
            tf.keras.initializers.get("glorot_uniform")
        ),
        "bias_initializer": tf.keras.initializers.serialize(
            tf.keras.initializers.get("glorot_uniform")
        ),
    }
    config = cell.get_config()
    assert config == expected_config
    restored_cell = ESNCell.from_config(config)
    restored_config = restored_cell.get_config()
    assert config == restored_config