code: def unscale(self, data):
      data_unscale = torch.zeros(data.size(), dtype=torch.float64)
      for i in self.scaler_index:
          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_unscale[:, :, i] = (
              data[:, :, i] * value_scale + value_mean
          )
      return data_unscale
  
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 3 for calling the ExportWithStandardScaler obj's underlying member method code with example usage: obj.unscale(data) and returns: data_unscale