docstr: Select features and then return X with the selected features.
  
  Parameters
  ----------
  X : array-like
      Shape = [n_samples, n_features]
      The training input samples.
  y : array-like
      Shape = [n_samples]
      The target values (class labels in classification, real numbers in
      regression).
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code: def fit_transform(self, X, y):
      """
      Select features and then return X with the selected features.
  
      Parameters
      ----------
      X : array-like
          Shape = [n_samples, n_features]
          The training input samples.
      y : array-like
          Shape = [n_samples]
          The target values (class labels in classification, real numbers in
          regression).
      """
      self.fit(X, y)
      return self.transform(X)
  
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 16 for calling the FeatureGradientSelector obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: self