code: def test_calculate_shape_check(
      mus: np.ndarray,
      prior_weight: float,
      prior: bool,
      magic_clip: bool,
      endpoints: bool,
      multivariate: bool,
  ) -> None:
      parameters = _ParzenEstimatorParameters(
          prior_weight=prior_weight,
          consider_prior=prior,
          consider_magic_clip=magic_clip,
          consider_endpoints=endpoints,
          weights=default_weights,
          multivariate=multivariate,
          categorical_distance_func={},
      )
      mpe = _ParzenEstimator(
          {"a": mus},
          {"a": distributions.FloatDistribution(-1.0, 1.0)},
          parameters,
      )
      assert len(mpe._mixture_distribution.weights) == max(
          len(mus) + int(prior), 1
      )
  
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 3 for calling a global function with example usage: test_calculate_shape_check(mus, prior_weight, prior, magic_clip, endpoints, multivariate) without return types