code: def test_gelu(self):
      def gelu(x, approximate=False):
          if approximate:
              return (
                  0.5
                  * x
                  * (
                      1.0
                      + np.tanh(
                          np.sqrt(2.0 / np.pi)
                          * (x + 0.044715 * np.power(x, 3))
                      )
                  )
              )
          else:
              from scipy.stats import norm
  
              return x * norm.cdf(x)
  
      x = np.random.random((2, 5))
      result = activations.gelu(x[np.newaxis, :])[0]
      expected = gelu(x)
      self.assertAllClose(result, expected, rtol=1e-05)
  
      x = np.random.random((2, 5))
      result = activations.gelu(x[np.newaxis, :], approximate=True)[0]
      expected = gelu(x, True)
      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 33 for calling the ActivationsTest obj's underlying member method code with example usage: obj.test_gelu() and returns: unknown, unknown