code: def test_calculate(
      mus: np.ndarray,
      flags: Dict[str, bool],
      expected: Dict[str, List[float]],
  ) -> None:
      parameters = _ParzenEstimatorParameters(
          prior_weight=1.0,
          consider_prior=flags["prior"],
          consider_magic_clip=flags["magic_clip"],
          consider_endpoints=flags["endpoints"],
          weights=default_weights,
          multivariate=False,
          categorical_distance_func={},
      )
      mpe = _ParzenEstimator(
          {"a": mus},
          {"a": distributions.FloatDistribution(-1.0, 1.0)},
          parameters,
      )
      expected_distribution = _MixtureOfProductDistribution(
          weights=np.asarray(expected["weights"]),
          distributions=[
              _BatchedTruncNormDistributions(
                  mu=np.asarray(expected["mus"]),
                  sigma=np.asarray(expected["sigmas"]),
                  low=-1.0,
                  high=1.0,
              )
          ],
      )
      assert_distribution_almost_equal(
          mpe._mixture_distribution, expected_distribution
      )
  
imports: from typing import Callable
  from typing import Dict
  from typing import List
  import numpy
  import pytest
  from optuna import distributions
  from optuna.distributions import CategoricalChoiceType
  from optuna.samplers._tpe.parzen_estimator import _ParzenEstimator
  from optuna.samplers._tpe.parzen_estimator import _ParzenEstimatorParameters
  from optuna.samplers._tpe.probability_distributions import _BatchedCategoricalDistributions
  from optuna.samplers._tpe.probability_distributions import _BatchedDiscreteTruncNormDistributions
  from optuna.samplers._tpe.probability_distributions import _BatchedTruncNormDistributions
  from optuna.samplers._tpe.probability_distributions import _MixtureOfProductDistribution
  from optuna.samplers._tpe.sampler import default_weights
label_desc: Use image node_id 6 for calling a global function with example usage: test_calculate(mus, flags, expected) without return types