code: def seed_generator():
      # Seed must be passed as kwarg.
      a = yield trainable_state_util.Parameter(
          functools.partial(samplers.normal, shape=[5])
      )
      # Seed must be passed positionally.
      b = yield trainable_state_util.Parameter(
          lambda my_seed: samplers.normal([], seed=my_seed)
      )
      # Seed not accepted.
      c = yield trainable_state_util.Parameter(lambda: tf.zeros([3]))
      # Bare value in place of callable.
      d = yield trainable_state_util.Parameter(tf.ones([1, 1]))
      # Distribution sample method.
      e = yield trainable_state_util.Parameter(
          lognormal.LogNormal([-1.0, 1.0], 1.0).sample
      )
      return jds.JointDistributionSequential(
          [
              deterministic.Deterministic(a),
              deterministic.Deterministic(b),
              deterministic.Deterministic(c),
              deterministic.Deterministic(d),
              deterministic.Deterministic(e),
          ]
      )
  
imports: import functools
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import joint_map
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import deterministic
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import prefer_static
  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.internal import trainable_state_util
  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 1 for calling a global function with example usage: seed_generator() and returns: jds