code: def test_mcmc_helper_target_function_no_transforms(
      prior_on: PriorOn,
  ) -> None:
      """Verifies the objective for a set of priors where no transforms are set."""
      expected_log_prior = 0.0
      prior_width = 200.0
  
      data = build_data()
      model = build_model_with_uniform_prior_no_transforms(
          data, prior_on, prior_width
      )
  
      hmc_helper = gpflow.optimizers.SamplingHelper(
          model.log_posterior_density, model.trainable_parameters
      )
  
      for _ in model.trainable_parameters:
          prior_density = 1 / prior_width
          expected_log_prior += np.log(prior_density)
  
      log_marginal_likelihood = model.log_marginal_likelihood().numpy()
      expected_log_prob = log_marginal_likelihood + expected_log_prior
      target_log_prob_fn = hmc_helper.target_log_prob_fn
  
      np.testing.assert_allclose(
          target_log_prob_fn(), expected_log_prob
      )
  
      # Test the wrapped closure
      log_prob, grad_fn = target_log_prob_fn.__original_wrapped__()  # type: ignore[attr-defined]
      grad, nones = grad_fn(1, [None] * len(model.trainable_parameters))
      assert len(grad) == len(model.trainable_parameters)
      assert nones == [None] * len(model.trainable_parameters)
  
imports: from typing import Tuple
  import numpy
  import pytest
  import tensorflow
  import tensorflow_probability
  from check_shapes import ShapeChecker, check_shapes
  from tensorflow_probability.python.distributions import Gamma, Uniform
  import gpflow
  from gpflow.base import AnyNDArray, PriorOn
  from gpflow.models import GPR
  from gpflow.utilities import to_default_float
label_desc: Use image node_id 7 for calling a global function with example usage: test_mcmc_helper_target_function_no_transforms(prior_on) without return types