code: def _constrained_zeros_fn(shape, dtype, constraint_fn):
      """Generates dummy parameters initialized to a valid default value."""
      return hps.just(
          constraint_fn(tf.fill(shape, tf.cast(0.0, dtype)))
      )
  
imports: from absl.testing import parameterized
  import hypothesis
  from hypothesis import strategies
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import hypothesis_testlib
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import markov_chain
  from tensorflow_probability.python.distributions import mvn_tril
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.experimental.bijectors import distribution_bijectors
  from tensorflow_probability.python.experimental.vi import automatic_structured_vi
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.mcmc import dual_averaging_step_size_adaptation
  from tensorflow_probability.python.mcmc import nuts
  from tensorflow_probability.python.mcmc import sample
  from tensorflow_probability.python.mcmc import transformed_kernel
  from tensorflow_probability.python.vi import optimization
label_desc: Use image node_id 1 for calling a global function with example usage: _constrained_zeros_fn(shape, dtype, constraint_fn) and returns: hps