code: def scale(self, data):
      data_scale = torch.zeros(data.size(), dtype=torch.float64)
      for i in range(data.size()[1]):
          value_mean = (
              self.mean_[i]
              if self.with_mean
              else torch.zeros(self.mean_.size(), dtype=torch.float64)
          )
          value_scale = (
              self.scale_[i]
              if self.with_std
              else torch.ones(self.scale_.size(), dtype=torch.float64)
          )
          data_scale[:, i] = (data[:, i] - value_mean) / value_scale
      return data_scale
  
imports: import torch
  import torch.nn
  from typing import List
  from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler, RobustScaler
  from .utils import _to_list
label_desc: Use image node_id 2 for calling the ExportWithStandardScaler obj's underlying member method code with example usage: obj.scale(data) and returns: data_scale