code: def testGeneralizedNormalCDF(self):
      batch_size = 50
      mu = self._rng.randn(batch_size)
      sigma = self._rng.rand(batch_size) + 1.0
      power = self._rng.rand(batch_size) + 1.0
      x = np.linspace(-8.0, 8.0, batch_size).astype(np.float64)
  
      gnormal = generalized_normal.GeneralizedNormal(
          loc=self.make_input(mu),
          scale=self.make_input(sigma),
          power=self.make_input(power),
          validate_args=True,
      )
      cdf = gnormal.cdf(x)
      self.assertAllEqual(
          self.evaluate(gnormal.batch_shape_tensor()), cdf.shape
      )
      self.assertAllEqual(
          self.evaluate(gnormal.batch_shape_tensor()),
          self.evaluate(cdf).shape,
      )
      self.assertAllEqual(gnormal.batch_shape, cdf.shape)
      self.assertAllEqual(gnormal.batch_shape, self.evaluate(cdf).shape)
      expected_cdf = sp_stats.gennorm(power, loc=mu, scale=sigma).cdf(x)
      self.assertAllClose(
          expected_cdf, self.evaluate(cdf), atol=0, rtol=1e-5
      )
  
imports: import math
  import numpy
  from scipy import stats
  from scipy.special import gamma
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import generalized_normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 7 for calling the _GeneralizedNormalTest obj's underlying member method code with example usage: obj.testGeneralizedNormalCDF() without return types