code: def testGeneralizedNormalShapeWithPlaceholders(self):
      mu = tf.Variable(np.float32(5), shape=tf.TensorShape(None))
      scale = tf.Variable(
          np.float32([1.0, 2.0]), shape=tf.TensorShape(None)
      )
      power = tf.Variable(
          np.float32([[1.0, 2.0]]).T, shape=tf.TensorShape(None)
      )
      self.evaluate(
          [mu.initializer, scale.initializer, power.initializer]
      )
      gnormal = generalized_normal.GeneralizedNormal(
          loc=mu, scale=scale, power=power, validate_args=True
      )
  
      # get_batch_shape should return an '<unknown>' tensor (graph mode only).
      self.assertEqual(gnormal.event_shape, ())
      self.assertEqual(gnormal.batch_shape, tf.TensorShape(None))
      self.assertAllEqual(
          self.evaluate(gnormal.event_shape_tensor()), []
      )
      self.assertAllEqual(
          self.evaluate(gnormal.batch_shape_tensor()), [2, 2]
      )
  
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 21 for calling the _GeneralizedNormalTest obj's underlying member method code with example usage: obj.testGeneralizedNormalShapeWithPlaceholders() without return types