code: def __init__(self, name="output_to_unconstrained"):
      parameters = dict(locals())
      with tf.name_scope(name) as name:
          super(OutputToUnconstrained, self).__init__(
              validate_args=True,
              forward_min_event_ndims=2,
              inverse_min_event_ndims=1,
              parameters=parameters,
              name=name,
          )
  
imports: import itertools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import correlation_cholesky
  from tensorflow_probability.python.bijectors import fill_triangular
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import cholesky_lkj
  from tensorflow_probability.python.distributions import lkj
  from tensorflow_probability.python.distributions.internal import statistical_testing
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.mcmc import hmc
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import transformed_kernel
label_desc: Use image node_id 1 to create a new OutputToUnconstrained object from inherited base classes: bijector_lib with example: obj = OutputToUnconstrained(name)