docstr: Process the results of a run into something suitable for transform().
docstr_tok: ["Process","the","results","of","a","run","into","something","suitable","for","transform","(",")","."]
code: def _process_results(self, m, solver, X, groups=None):
      """
      Process the results of a run into something suitable for transform().
      """
      self.scores_ = self._process_torch_scores(
          torch.sigmoid(m[constants.Checkpoint.MODEL]["x"] * 2)
      )
      if self.max_features:
          self.max_features = min(
              [self.max_features, self.scores_.shape[0]]
          )
          n_features = self._recommend_number_features(solver)
          self.set_n_features(n_features, groups=groups)
      elif self.n_features:
          self.set_n_features(self.n_features, groups=groups)
      else:
          self.selected_features_ = m["feats"]
  
      # subtract elapsed time from max_time
      self.max_time -= m["t"]
  
      self.model_ = m
  
      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 10 for calling the FeatureGradientSelector obj's underlying member method code with example usage: obj._process_results(m, solver, X, groups) and returns: self