docstr: Get parameters for this estimator.
docstr_tok: ["Get","parameters","for","this","estimator","."]
code: def get_params(self, deep=True):
      """
      Get parameters for this estimator.
      """
      params = self.__dict__
      params = {
          key: val
          for (key, val) in params.items()
          if not key.endswith("_")
      }
      return params
  
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 14 for calling the FeatureGradientSelector obj's underlying member method code with example usage: obj.get_params(deep) and returns: params