code: def _call_weights_func(
      weights_func: Callable[[int], np.ndarray], n: int
  ) -> np.ndarray:
      w = np.array(weights_func(n))[:n]
      if np.any(w < 0):
          raise ValueError(
              f"The `weights` function is not allowed to return negative values {w}. "
              + f"The argument of the `weights` function is {n}."
          )
      if len(w) > 0 and np.sum(w) <= 0:
          raise ValueError(
              f"The `weight` function is not allowed to return all-zero values {w}."
              + f" The argument of the `weights` function is {n}."
          )
      if not np.all(np.isfinite(w)):
          raise ValueError(
              "The `weights`function is not allowed to return infinite or NaN values "
              + f"{w}. The argument of the `weights` function is {n}."
          )
  
      # TODO(HideakiImamura) Raise `ValueError` if the weight function returns an ndarray of
      # unexpected size.
      return w
  
imports: from typing import Callable
  from typing import Dict
  from typing import NamedTuple
  from typing import Optional
  import numpy
  from optuna.distributions import BaseDistribution
  from optuna.distributions import CategoricalChoiceType
  from optuna.distributions import CategoricalDistribution
  from optuna.distributions import FloatDistribution
  from optuna.distributions import IntDistribution
  from optuna.samplers._tpe.probability_distributions import _BatchedCategoricalDistributions
  from optuna.samplers._tpe.probability_distributions import _BatchedDiscreteTruncNormDistributions
  from optuna.samplers._tpe.probability_distributions import _BatchedDistributions
  from optuna.samplers._tpe.probability_distributions import _BatchedTruncNormDistributions
  from optuna.samplers._tpe.probability_distributions import _MixtureOfProductDistribution
label_desc: Use image node_id 4 for calling the _ParzenEstimator obj's underlying member method code with example usage: obj._call_weights_func(weights_func, n) and returns: w