code: def _standard_scaler_scale_timeseries_numpy(data, scaler):
      data_scale = np.zeros(data.shape)
      feature_counter = 0
      for i in range(data.shape[1]):
          value_mean = scaler.mean_[i] if scaler.with_mean else 0
          value_scale = scaler.scale_[i] if scaler.with_std else 1
          data_scale[:, i] = (data[:, i] - value_mean) / value_scale
      return data_scale
  
file_path: BigDL/python/chronos/src/bigdl/chronos/data/utils/scale.py
imports: import numpy
  import pandas
  from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler, RobustScaler
label_desc: Use image node_id 6 for calling a global function with example usage: _standard_scaler_scale_timeseries_numpy(data, scaler) and returns: data_scale