code: def testEventShape(self):
      scale = make_pd(1.0, 2)
      chol_scale = chol(scale)
  
      w = wishart.WishartTriL(
          df=4, scale_tril=chol_scale, validate_args=True
      )
      self.assertAllEqual([2, 2], w.event_shape)
      self.assertAllEqual([2, 2], self.evaluate(w.event_shape_tensor()))
  
      w = wishart.WishartTriL(
          df=[4.0, 4],
          scale_tril=np.array([chol_scale, chol_scale]),
          validate_args=True,
      )
      self.assertAllEqual([2, 2], w.event_shape)
      self.assertAllEqual([2, 2], self.evaluate(w.event_shape_tensor()))
  
      scale_deferred = tf1.placeholder_with_default(
          chol_scale, shape=chol_scale.shape
      )
      w = wishart.WishartTriL(
          df=4, scale_tril=scale_deferred, validate_args=True
      )
      self.assertAllEqual([2, 2], self.evaluate(w.event_shape_tensor()))
  
      scale_deferred = tf1.placeholder_with_default(
          np.array([chol_scale, chol_scale]), shape=None
      )
      w = wishart.WishartTriL(
          df=4, scale_tril=scale_deferred, validate_args=True
      )
      self.assertAllEqual([2, 2], self.evaluate(w.event_shape_tensor()))
  
imports: import numpy
  from scipy import linalg
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import wishart
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 17 for calling the WishartTest obj's underlying member method code with example usage: obj.testEventShape() without return types