code: def testParamBroadcasting(self):
      # A 1-batch of 2x2 scale operators
      scale_tril = np.expand_dims(chol([[1.0, 0.5], [0.5, 1.0]]), 0)
  
      df = [5, 6, 7]  # A 3-batch of degrees of freedom
      wish = wishart.WishartTriL(df=df, scale_tril=scale_tril)
      self.assertAllEqual(
          [2, 2], tensorshape_util.as_list(wish.event_shape)
      )
      self.assertAllEqual(
          [2, 2], self.evaluate(wish.event_shape_tensor())
      )
      self.assertAllEqual(
          [3], tensorshape_util.as_list(wish.batch_shape)
      )
      self.assertAllEqual([3], self.evaluate(wish.batch_shape_tensor()))
      self.assertAllEqual(
          [4, 3, 2, 2],
          wish.sample(
              sample_shape=(4,), seed=test_util.test_seed()
          ).shape,
      )
      self.assertAllEqual(
          [4, 3, 2, 2],
          self.evaluate(
              tf.shape(
                  wish.sample(
                      sample_shape=(4,), seed=test_util.test_seed()
                  )
              )
          ),
      )
  
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 3 for calling the WishartTest obj's underlying member method code with example usage: obj.testParamBroadcasting() without return types