code: def __training_data_process__(self, data, label):
      # crop data according net input size
      data = data.get_data()
      label = label.get_data()
  
      # drop out the invalid range
      data, label = self.__drop_invalid_range__(data, label)
  
      # crop data
      data, label = self.__crop_data__(data, label)
  
      # resize data
      data = self.__resize_data__(data)
      label = self.__resize_data__(label)
  
      # normalization datas
      data = self.__itensity_normalize_one_volume__(data)
  
      return data, label
  
file_path: MedicalNet/datasets/brains18.py
imports: import math
  import os
  import random
  import numpy
  from torch.utils.data import Dataset
  import nibabel
  from scipy import ndimage
label_desc: Use image node_id 10 for calling the BrainS18Dataset obj's underlying member method code with example usage: obj.__training_data_process__(data, label) and returns: data,  label