class_docstr: Soft Tophat prior as it used in the original spearmint code.
  
  Parameters
  ----------
  lower_bound : float
      Lower bound of the prior. In original scale.
  upper_bound : float
      Upper bound of the prior. In original scale.
  exponent : float
      Exponent of the prior.
  seed : int, defaults to 0
code: def _sample_from_prior(self, n_samples: int) -> np.ndarray:
      return np.exp(
          self._rng.uniform(
              self._log_lower_bound,
              self._log_upper_bound,
              size=(n_samples,),
          )
      )
  
imports: from __future__ import annotations
  from typing import Any
  import warnings
  import numpy
  from smac.model.gaussian_process.priors.abstract_prior import AbstractPrior
label_desc: Use image node_id 8 for calling the SoftTopHatPrior obj's underlying member method code with example usage: obj._sample_from_prior(n_samples) and returns: np