code: def nested_lists_model_fn():
      return collections.OrderedDict(
          (
              (
                  "abc",
                  jds.JointDistributionSequential(
                      [
                          mvn_diag.MultivariateNormalDiag(
                              [0.0, 0.0], [1.0, 1.0]
                          ),
                          jds.JointDistributionSequential(
                              [
                                  student_t.StudentT(3.0, -2.0, 5.0),
                                  exponential.Exponential(4.0),
                              ]
                          ),
                      ]
                  ),
              ),
              (
                  "de",
                  lambda abc: jds.JointDistributionSequential(
                      [  # pylint: disable=g-long-lambda
                          independent.Independent(
                              normal.Normal(
                                  abc[0] * abc[1][0], abc[1][1]
                              ),
                              reinterpreted_batch_ndims=1,
                          ),
                          independent.Independent(
                              normal.Normal(
                                  abc[0] + abc[1][0], abc[1][1]
                              ),
                              reinterpreted_batch_ndims=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 2 for calling a global function with example usage: nested_lists_model_fn() and returns: collections