class_docstr: TimeSequence feature engineering
docstr: Fit data and transform the raw data to features. This is used in training for hyper
  parameter searching.
  This method will refresh the parameters (e.g. min and max of the MinMaxScaler) if any
  :param input_df: The input time series 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
  :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 _fit_transform(self, input_df):
      """
      Fit data and transform the raw data to features. This is used in training for hyper
      parameter searching.
      This method will refresh the parameters (e.g. min and max of the MinMaxScaler) if any
      :param input_df: The input time series 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
      :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
      """
      self._check_input(input_df, mode="train")
      # print(input_df.shape)
      feature_data = self._get_features(input_df, self.config)
      self.scaler.fit(feature_data)
      data_n = self._scale(feature_data)
      assert np.mean(data_n[0]) < 1e-5
      (x, y) = self._roll_train(
          data_n,
          past_seq_len=self.past_seq_len,
          future_seq_len=self.future_seq_len,
      )
  
      return x, y
  
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 2 for calling the TimeSequenceFeatureTransformer obj's underlying member method code with example usage: obj._fit_transform(input_df) and returns: x,  y