docstr: Reset the estimator by deleting all private and fit parameters.
docstr_tok: ["Reset","the","estimator","by","deleting","all","private","and","fit","parameters","."]
code: def _reset(self):
      """
      Reset the estimator by deleting all private and fit parameters.
      """
      params = self.__dict__
      for key, _ in params.items():
          if key.endswith("_") or key.startswith("_"):
              delattr(self, key)
      return self
  
imports: import time
  import numpy
  import pandas
  from sklearn.base import BaseEstimator
  from sklearn.feature_selection import SelectorMixin
  from sklearn.utils.validation import check_is_fitted
  import torch
  from nni.feature_engineering.feature_selector import FeatureSelector
  from .None import constants
  from .fginitialize import PrepareData
  from .fgtrain import _train
label_desc: Use image node_id 25 for calling the FeatureGradientSelector obj's underlying member method code with example usage: obj._reset() and returns: self