code: def test_leaky_relu(self):
      leaky_relu_vectorized = np.vectorize(_ref_leaky_relu)
  
      # Test for negative_slope = 0.01
      # Test positive values
      positive_values = np.random.random((2, 5))
      result = activations.leaky_relu(
          positive_values[np.newaxis, :], negative_slope=0.01
      )[0]
      expected = leaky_relu_vectorized(positive_values, alpha=0.01)
      self.assertAllClose(result, expected, rtol=1e-05)
  
      # Test negative values
      negative_values = np.random.uniform(-1, 0, (2, 5))
      result = activations.leaky_relu(
          negative_values[np.newaxis, :], negative_slope=0.01
      )[0]
      expected = leaky_relu_vectorized(negative_values, alpha=0.01)
      self.assertAllClose(result, expected, rtol=1e-05)
  
      # Test for negative_slope = 0.3
      # Test positive values
      positive_values = np.random.random((2, 5))
      result = activations.leaky_relu(
          positive_values[np.newaxis, :], negative_slope=0.3
      )[0]
      expected = leaky_relu_vectorized(positive_values, alpha=0.3)
      self.assertAllClose(result, expected, rtol=1e-05)
  
      # Test negative values
      negative_values = np.random.uniform(-1, 0, (2, 5))
      result = activations.leaky_relu(
          negative_values[np.newaxis, :], negative_slope=0.3
      )[0]
      expected = leaky_relu_vectorized(negative_values, alpha=0.3)
      self.assertAllClose(result, expected, rtol=1e-05)
  
imports: import numpy
  from keras import activations
  from keras import backend
  from keras import testing
label_desc: Use image node_id 30 for calling the ActivationsTest obj's underlying member method code with example usage: obj.test_leaky_relu() without return types