code: def testGeneralizedNormalShape(self):
      mu = tf.constant([-3.0] * 5, self.dtype)
      sigma = tf.constant(11.0, self.dtype)
      power = tf.constant(1.0, self.dtype)
      normal = generalized_normal.GeneralizedNormal(
          loc=mu, scale=sigma, power=power, validate_args=True
      )
  
      self.assertEqual(self.evaluate(normal.batch_shape_tensor()), [5])
      self.assertEqual(normal.batch_shape, tf.TensorShape([5]))
      self.assertAllEqual(
          self.evaluate(normal.event_shape_tensor()), []
      )
      self.assertEqual(normal.event_shape, tf.TensorShape([]))
  
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 20 for calling the _GeneralizedNormalTest obj's underlying member method code with example usage: obj.testGeneralizedNormalShape() without return types