code: def part_dists():
      d0 = gamma.Gamma(1.0, 1.0, name="w")
      d1 = gamma.Gamma(1.0, 2.0, name="x")
      d2 = lambda x: sample.Sample(  # pylint:disable=g-long-lambda
          uniform.Uniform(x, x + np.float32(10.0)), 2, name="y"
      )
  
      def d3(y, x, w):
          return independent.Independent(
              cholesky_lkj.CholeskyLKJ(
                  4, concentration=y + (w + x)[..., tf.newaxis]
              ),
              reinterpreted_batch_ndims=1,
              name="z",
          )
  
      return d0, d1, d2, d3
  
imports: import collections
  import functools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import pad
  from tensorflow_probability.python.distributions import beta
  from tensorflow_probability.python.distributions import binomial
  from tensorflow_probability.python.distributions import cholesky_lkj
  from tensorflow_probability.python.distributions import gamma
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import joint_distribution_named
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import multinomial
  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 uniform
  from tensorflow_probability.python.experimental.distributions import increment_log_prob
  from tensorflow_probability.python.experimental.distributions import joint_distribution_pinned
  from tensorflow_probability.python.internal import structural_tuple
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import generic
label_desc: Use image node_id 1 for calling a global function with example usage: part_dists() and returns: d0,  d1,  d2,  d3, independent