code: def testShape(self):
      concentration = tf.constant([3.0] * 5)
      rate = tf.constant(11.0)
      d = exp_gamma_lib.ExpGamma(
          concentration=concentration, rate=rate, validate_args=True
      )
  
      self.assertEqual(self.evaluate(d.batch_shape_tensor()), (5,))
      self.assertEqual(d.batch_shape, tf.TensorShape([5]))
      self.assertAllEqual(self.evaluate(d.event_shape_tensor()), [])
      self.assertEqual(d.event_shape, tf.TensorShape([]))
  
imports: from absl.testing import parameterized
  import numpy
  from scipy import misc
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.distributions import exp_gamma
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 1 for calling the ExpGammaTest obj's underlying member method code with example usage: obj.testShape() without return types