code: def verify_expectations(self, d):
      shape_ = np.array([int(1e6)], np.int32)
      shape = (
          tf.constant(shape_)
          if self.use_static_shape
          else tf1.placeholder_with_default(shape_, shape=None)
      )
      # This shape will require broadcasting before sampling.
      dimension = (
          tf.constant(d)
          if self.use_static_shape
          else tf1.placeholder_with_default(d, shape=None)
      )
      x = random_ops.spherical_uniform(
          dimension=dimension,
          shape=shape,
          dtype=self.dtype,
          seed=test_util.test_seed(),
      )
      self.assertEqual(self.dtype, dtype_util.as_numpy_dtype(x.dtype))
      final_shape_ = [int(1e6), d]
      if self.use_static_shape:
          self.assertAllEqual(final_shape_, x.shape)
      sample_mean = tf.reduce_mean(x, axis=0, keepdims=True)
      sample_covar = sample_stats.covariance(x)
      [x_, sample_mean_, sample_covar_] = self.evaluate(
          [x, sample_mean, sample_covar]
      )
      self.assertAllEqual(final_shape_, x_.shape)
      self.assertAllClose(
          np.zeros_like(sample_mean_),
          sample_mean_,
          atol=2e-3,
          rtol=1e-3,
      )
      self.assertAllClose(
          np.eye(d, dtype=self.dtype) / d,
          sample_covar_,
          atol=2e-3,
          rtol=1e-2,
      )
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.random import random_ops
  from tensorflow_probability.python.stats import sample_stats
label_desc: Use image node_id 1 for calling the _RandomSphericalUniform obj's underlying member method code with example usage: obj.verify_expectations(d) without return types