code: def test_rrelu(dtype, training):
      x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=dtype)
      lower = 0.1
      upper = 0.2
      training_results = {
          np.float16: [-0.3826, -0.165, 0, 1, 2],
          np.float32: [-0.282151192, -0.199812651, 0, 1, 2],
          np.float64: [-0.25720977, -0.1221586, 0, 1, 2],
      }
      result = rrelu(
          x,
          lower,
          upper,
          training=training,
          seed=None,
          rng=tf.random.Generator.from_seed(SEED),
      )
      if training:
          expect_result = training_results.get(dtype)
      else:
          expect_result = [
              -0.30000001192092896,
              -0.15000000596046448,
              0,
              1,
              2,
          ]
      test_utils.assert_allclose_according_to_type(
          result, expect_result
      )
  
imports: import pytest
  import numpy
  import tensorflow
  from tensorflow_addons.activations import rrelu
  from tensorflow_addons.utils import test_utils
label_desc: Use image node_id 2 for calling a global function with example usage: test_rrelu(dtype, training) without return types