class_docstr: TimeSequence feature engineering
code: def _get_features(self, input_df, config):
      feature_matrix = self._generate_features(input_df)
      selected_features = config.get("selected_features")
      if selected_features:
          feature_cols = np.asarray(json.loads(selected_features))
      else:
          feature_cols = self.get_feature_list()
      # we do not include target col in candidates.
      # the first column is designed to be the default position of target column.
      target_col = np.array(self.target_col)
      cols = np.concatenate([target_col, feature_cols])
      target_feature_matrix = feature_matrix[cols]
      return target_feature_matrix.astype(float)
  
imports: from bigdl.chronos.autots.deprecated.feature.utils import save_config
  from bigdl.chronos.autots.deprecated.feature.abstract import BaseFeatureTransformer
  from bigdl.chronos.utils import deprecated
  import sklearn
  from sklearn.preprocessing import StandardScaler
  import pandas
  import numpy
  import json
  from packaging import version
label_desc: Use image node_id 25 for calling the TimeSequenceFeatureTransformer obj's underlying member method code with example usage: obj._get_features(input_df, config) and returns: target_feature_matrix