class_docstr: An error evaluator that always returns a constant value.
  
  This evaluator can be used to terminate the optimization when the evaluated improvement
  potential is below the fixed threshold.
  
  Args:
      constant:
          A user-specified constant value to always return as an error estimate.
class_docstr_tok: ["An","error","evaluator","that","always","returns","a","constant","value",".","This","evaluator","can","be","used","to","terminate","the","optimization","when","the","evaluated","improvement","potential","is","below","the","fixed","threshold",".","Args",":","constant",":","A","user-specified","constant","value","to","always","return","as","an","error","estimate","."]
code: def evaluate(
      self,
      trials: list[FrozenTrial],
      study_direction: StudyDirection,
  ) -> float:
      return self._constant
  
file_path: optuna/optuna/terminator/erroreval.py
imports: from __future__ import annotations
  import abc
  from typing import cast
  import numpy
  from optuna._experimental import experimental_class
  from optuna.study import StudyDirection
  from optuna.trial import FrozenTrial
  from optuna.trial import Trial
  from optuna.trial._state import TrialState
label_desc: Use image node_id 2 for calling the StaticErrorEvaluator obj's underlying member method code with example usage: obj.evaluate(trials, study_direction) and returns: self