class_docstr: TimeSequence feature engineering
docstr: Transform data into features using the preset of configurations from fit_transform
  :param input_df: The input time series data frame, input_df can be a list of data frame or
                   one data frame.
   Example:
   datetime   value   "extra feature 1"   "extra feature 2"
   2019-01-01  1.9         1                       2
   2019-01-02  2.3         0                       2
  :param is_train: If the input_df is for training.
  :return: tuple (x,y)
      x: 3-d array in format (no. of samples, past sequence length, 2+feature length),
      in the last dimension, the 1st col is the time index
      (data type needs to be numpy datetime type, e.g. "datetime64"),
      the 2nd col is the target value (data type should be numeric)
      y: y is 2-d numpy array in format (no. of samples, future sequence length)
      if future sequence length > 1, or 1-d numpy array in format (no. of samples, )
      if future sequence length = 1
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code: def transform(self, input_df, is_train=True):
      """
      Transform data into features using the preset of configurations from fit_transform
      :param input_df: The input time series data frame, input_df can be a list of data frame or
                       one data frame.
       Example:
       datetime   value   "extra feature 1"   "extra feature 2"
       2019-01-01  1.9         1                       2
       2019-01-02  2.3         0                       2
      :param is_train: If the input_df is for training.
      :return: tuple (x,y)
          x: 3-d array in format (no. of samples, past sequence length, 2+feature length),
          in the last dimension, the 1st col is the time index
          (data type needs to be numpy datetime type, e.g. "datetime64"),
          the 2nd col is the target value (data type should be numeric)
          y: y is 2-d numpy array in format (no. of samples, future sequence length)
          if future sequence length > 1, or 1-d numpy array in format (no. of samples, )
          if future sequence length = 1
      """
      from bigdl.nano.utils.common import invalidInputError
  
      if self.config is None or self.past_seq_len is None:
          invalidInputError(
              False,
              "Needs to call fit_transform or restore"
              " first before calling transform",
          )
      mode = "val" if is_train else "test"
      if isinstance(input_df, list):
          output_x_list = []
          output_y_list = []
          for df in input_df:
              if mode == "val":
                  x, y = self._transform(df, mode)
                  output_x_list.append(x)
                  output_y_list.append(y)
              else:
                  x, _ = self._transform(df, mode)
                  output_x_list.append(x)
          output_x = np.concatenate(output_x_list, axis=0)
          if output_y_list:
              output_y = np.concatenate(output_y_list, axis=0)
          else:
              output_y = None
      else:
          output_x, output_y = self._transform(input_df, mode)
      return output_x, output_y
  
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 5 for calling the TimeSequenceFeatureTransformer obj's underlying member method code with example usage: obj.transform(input_df, is_train) and returns: output_x,  output_y