code: def test_expected_value(self):
      shape_ = np.array([2, 3, int(1e3)], np.int32)
      shape = (
          tf.constant(shape_)
          if self.use_static_shape
          else tf1.placeholder_with_default(shape_, shape=None)
      )
      x = random_ops.rademacher(
          shape, self.dtype, seed=test_util.test_seed()
      )
      if self.use_static_shape:
          self.assertAllEqual(shape_, x.shape)
      x_ = self.evaluate(x)
      self.assertEqual(self.dtype, dtype_util.as_numpy_dtype(x.dtype))
      self.assertAllEqual(shape_, x_.shape)
      self.assertAllEqual([-1.0, 1], np.unique(np.reshape(x_, [-1])))
      self.assertAllClose(
          np.zeros(shape_[:-1]),
          np.mean(x_, axis=-1),
          atol=0.07,
          rtol=0.0,
      )
  
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 _RandomRademacher obj's underlying member method code with example usage: obj.test_expected_value() without return types