code: def test_hard_silu(self):
      # Basic test for random values between -3 and 3
      x = np.random.uniform(-3, 3, (2, 5)).astype("float32")
      result = activations.hard_silu(x[np.newaxis, :])[0]
      expected = np.vectorize(_ref_hard_silu)(x)
      self.assertAllClose(result, expected, rtol=1e-05)
  
      # Test with 1D array
      x_1d = np.random.uniform(-10, 10, 5).astype("float32")
      result_1d = activations.hard_silu(x_1d)
      expected_1d = np.vectorize(_ref_hard_silu)(x_1d)
      self.assertAllClose(result_1d, expected_1d, rtol=1e-05)
  
      # Test with 3D array
      x_3d = np.random.uniform(-10, 10, (3, 3, 3)).astype("float32")
      result_3d = activations.hard_silu(x_3d)
      expected_3d = np.vectorize(_ref_hard_silu)(x_3d)
      self.assertAllClose(result_3d, expected_3d, rtol=1e-05)
  
      # Test with strictly positive values much larger than 3
      x_positive_above_3 = np.random.uniform(5, 10, (2, 5)).astype(
          "float32"
      )
      result_positive_above_3 = activations.hard_silu(
          x_positive_above_3
      )
      expected_positive_above_3 = x_positive_above_3
      self.assertAllClose(
          result_positive_above_3, expected_positive_above_3, rtol=1e-05
      )
  
      # Test with strictly negative values much smaller than -3
      x_negatives = np.random.uniform(-10, -5, (2, 5)).astype("float32")
      result = activations.hard_silu(x_negatives)
      expected_zeros = np.zeros_like(x_negatives)
      self.assertAllClose(result, expected_zeros, rtol=1e-05)
  
imports: import numpy
  from keras import activations
  from keras import backend
  from keras import testing
label_desc: Use image node_id 21 for calling the ActivationsTest obj's underlying member method code with example usage: obj.test_hard_silu() without return types