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 __init__(self, constant: float) -> None:
      self._constant = 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 1 to create a new StaticErrorEvaluator object from inherited base classes: BaseErrorEvaluator with example: obj = StaticErrorEvaluator(constant)