class_docstr: Pruner which never prunes trials.
  
  Example:
  
      .. testcode::
  
          import numpy as np
          from sklearn.datasets import load_iris
          from sklearn.linear_model import SGDClassifier
          from sklearn.model_selection import train_test_split
  
          import optuna
  
          X, y = load_iris(return_X_y=True)
          X_train, X_valid, y_train, y_valid = train_test_split(X, y)
          classes = np.unique(y)
  
  
          def objective(trial):
              alpha = trial.suggest_float("alpha", 0.0, 1.0)
              clf = SGDClassifier(alpha=alpha)
              n_train_iter = 100
  
              for step in range(n_train_iter):
                  clf.partial_fit(X_train, y_train, classes=classes)
  
                  intermediate_value = clf.score(X_valid, y_valid)
                  trial.report(intermediate_value, step)
  
                  if trial.should_prune():
                      assert False, "should_prune() should always return False with this pruner."
                      raise optuna.TrialPruned()
  
              return clf.score(X_valid, y_valid)
  
  
          study = optuna.create_study(direction="maximize", pruner=optuna.pruners.NopPruner())
          study.optimize(objective, n_trials=20)
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code: def prune(
      self,
      study: "optuna.study.Study",
      trial: "optuna.trial.FrozenTrial",
  ) -> bool:
      return False
  
imports: import optuna
  from optuna.pruners import BasePruner
label_desc: Use image node_id 1 for calling the NopPruner obj's underlying member method code with example usage: obj.prune(study, trial) and returns: False