code: def basic_ordered_model_fn():
      return collections.OrderedDict(
          (
              ("a", normal.Normal(0.0, 1.0)),
              (
                  "e",
                  independent.Independent(
                      exponential.Exponential(rate=[100, 120]),
                      reinterpreted_batch_ndims=1,
                  ),
              ),
              (
                  "x",
                  lambda e: gamma.Gamma(
                      concentration=e[..., 0], rate=e[..., 1]
                  ),
              ),
          )
      )
  
imports: import collections
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import half_normal
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_named
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import kullback_leibler
  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 sample
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 1 for calling a global function with example usage: basic_ordered_model_fn() and returns: collections