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 get_gradient(self, theta: float) -> float:  # noqa: D102
      if np.ndim(theta) == 0:
          if theta < self._log_lower_bound:
              return -self._exponent * (theta - self._log_lower_bound)
          elif theta > self._log_upper_bound:
              return self._exponent * (self._log_upper_bound - theta)
          else:
              return 0
      else:
          raise NotImplementedError()
  
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 5 for calling the SoftTopHatPrior obj's underlying member method code with example usage: obj.get_gradient(theta) and returns: unknown, unknown, int