code: def testLogProbBroadcastsX(self):
      dims = 2
      batch_shape = [2, 3]
      scale = np.float32(
          [
              [[1.0, 0.5], [0.5, 1.0]],
              [[0.5, 0.25], [0.25, 0.75]],
          ]
      )
      scale_tril = np.stack([chol(s) for s in scale])
      scale_tril = np.reshape(
          np.concatenate([scale_tril] * 3, axis=0),
          batch_shape + [dims, dims],
      )
      dist = wishart.WishartTriL(
          df=5, scale_tril=scale_tril, validate_args=True
      )
      x = np.random.randn(dims, dims)
      x = np.matmul(x, x.T)
      lp = dist.log_prob(x)
      lp_bc = dist.log_prob(x * np.ones([2, 3, 1, 1]))
      lp_, lp_bc_ = self.evaluate([lp, lp_bc])
      self.assertAllEqual(batch_shape, lp.shape)
      self.assertAllEqual(batch_shape, lp_.shape)
      self.assertAllEqual(batch_shape, lp_bc.shape)
      self.assertAllEqual(batch_shape, lp_bc_.shape)
      self.assertAllClose(lp_bc_, lp_)
  
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 21 for calling the WishartTest obj's underlying member method code with example usage: obj.testLogProbBroadcastsX() without return types