code: def test_invalid_prior_weight(
      prior_weight: float, mus: np.ndarray
  ) -> None:
      parameters = _ParzenEstimatorParameters(
          prior_weight=prior_weight,
          consider_prior=True,
          consider_magic_clip=False,
          consider_endpoints=False,
          weights=default_weights,
          multivariate=False,
          categorical_distance_func={},
      )
      with pytest.raises(ValueError):
          _ParzenEstimator(
              {"a": mus},
              {"a": distributions.FloatDistribution(-1.0, 1.0)},
              parameters,
          )
  
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 5 for calling a global function with example usage: test_invalid_prior_weight(prior_weight, mus) without return types