code: def test_can_specify_callable_initializer(self):
      def uniform_initializer(
          _, shape, dtype, seed, constraining_bijector
      ):
          return constraining_bijector.forward(
              tf.random.stateless_uniform(
                  constraining_bijector.inverse_event_shape_tensor(
                      shape
                  ),
                  dtype=dtype,
                  seed=seed,
              )
          )
  
      init_fn, _ = trainable.make_trainable_stateless(
          normal.Normal,
          initial_parameters=uniform_initializer,
          batch_and_event_shape=[3, 4],
          validate_args=True,
      )
      raw_parameters = init_fn(test_util.test_seed())
      for v in tf.nest.flatten(raw_parameters):
          # Unconstrained parameters should be uniform in [0, 1].
          self.assertAllGreater(v, 0.0)
          self.assertAllLess(v, 1.0)
  
imports: from absl.testing import parameterized
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import fill_scale_tril
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import scale_matvec_tril
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import dirichlet
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import geometric
  from tensorflow_probability.python.distributions import kumaraswamy
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.distributions import wishart
  from tensorflow_probability.python.experimental.util import trainable
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math.minimize import minimize
  from tensorflow_probability.python.math.minimize import minimize_stateless
label_desc: Use image node_id 4 for calling the TestStatelessTrainableDistributionsAndBijectors obj's underlying member method code with example usage: obj.test_can_specify_callable_initializer() and returns: constraining_bijector