code: def test_calculate_shape_check_categorical(
      mus: np.ndarray,
      prior_weight: float,
      prior: bool,
      categorical_distance_func: Dict[
          str,
          Callable[
              [CategoricalChoiceType, CategoricalChoiceType], float
          ],
      ],
  ) -> None:
      parameters = _ParzenEstimatorParameters(
          prior_weight=prior_weight,
          consider_prior=prior,
          consider_magic_clip=True,
          consider_endpoints=False,
          weights=default_weights,
          multivariate=False,
          categorical_distance_func=categorical_distance_func,
      )
      mpe = _ParzenEstimator(
          {"c": mus},
          {"c": distributions.CategoricalDistribution([0.0, 1.0, 2.0])},
          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 4 for calling a global function with example usage: test_calculate_shape_check_categorical(mus, prior_weight, prior, categorical_distance_func) without return types