code: def test_bijector_uniform(self):
      d = sample.Sample(uniform.Uniform(tf.zeros([5]), 1.0), 2)
      b = d.experimental_default_event_space_bijector()
      # This line used to raise:
      # InvalidArgumentError: Incompatible shapes: [5] vs. [5,2] [Op:Mul]
      self.evaluate(b.forward(0.5 * tf.ones([5, 2])))
      # This line used to raise:
      # InvalidArgumentError: Incompatible shapes: [5,2] vs. [5] [Op:Sub]
      self.evaluate(b.inverse(d.sample(seed=test_util.test_seed())))
      nchains = 3
      # This line used to raise:
      # InvalidArgumentError: Incompatible shapes: [3,5,2] vs. [5] [Op:Sub]
      self.evaluate(
          b.inverse(d.sample(nchains, seed=test_util.test_seed()))
      )
  
      init = tf.random.uniform(
          (nchains,)
          + d.batch_shape
          + b.inverse_event_shape(d.event_shape),
          minval=-2.0,
          maxval=2.0,
          seed=test_util.test_seed(),
      )
      # This line used to raise:
      # InvalidArgumentError: Incompatible shapes: [5] vs. [3,5,2] [Op:Mul]
      self.evaluate(b.forward(init))
      self.assertEqual(
          (nchains,) + d.batch_shape,
          self.evaluate(d.log_prob(b.forward(init))).shape,
      )
  
imports: import functools
  import os
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import scale_matvec_tril
  from tensorflow_probability.python.distributions import cholesky_lkj
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import log_prob_ratio
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 16 for calling the SampleDistributionTest obj's underlying member method code with example usage: obj.test_bijector_uniform() without return types