class_docstr: TimeSequence feature engineering
code: def _generate_features(self, input_df):
      df = input_df.copy()
      df["id"] = df.index + 1
      field = df[self.dt_col]
  
      # built in time features
      for attr in TIME_FEATURE:
          if attr == "WEEKOFYEAR" and version.parse(
              pd.__version__
          ) >= version.parse("1.1.0"):
              # DatetimeProperties.weekofyear has been deprecated since pandas 1.1.0,
              # convert to DatetimeIndex to fix, and call pd.Int64Index to return a index
              field_datetime = pd.to_datetime(
                  field.values.astype(np.int64)
              )
              df[attr + "({})".format(self.dt_col)] = pd.Int64Index(
                  field_datetime.isocalendar().week
              )
          else:
              df[attr + "({})".format(self.dt_col)] = getattr(
                  field.dt, attr.lower()
              )
  
      # additional time features
      hour = field.dt.hour
      weekday = field.dt.weekday
      df["IS_AWAKE" + "({})".format(self.dt_col)] = (
          (((hour >= 6) & (hour <= 23)) | (hour == 0))
          .astype(int)
          .values
      )
      df["IS_BUSY_HOURS" + "({})".format(self.dt_col)] = (
          (((hour >= 7) & (hour <= 9)) | (hour >= 16) & (hour <= 19))
          .astype(int)
          .values
      )
      df["IS_WEEKEND" + "({})".format(self.dt_col)] = (
          weekday >= 5
      ).values
  
      return df
  
imports: from zoo.chronos.autots.deprecated.feature.utils import save_config
  from zoo.chronos.autots.deprecated.feature.abstract import BaseFeatureTransformer
  from zoo.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 24 for calling the TimeSequenceFeatureTransformer obj's underlying member method code with example usage: obj._generate_features(input_df) and returns: df