docstr: normalize the itensity of an nd volume based on the mean and std of nonzeor region
  inputs:
      volume: the input nd volume
  outputs:
      out: the normalized nd volume
docstr_tok: ["normalize","the","itensity","of","an","nd","volume","based","on","the","mean","and","std","of","nonzeor","region","inputs",":","volume",":","the","input","nd","volume","outputs",":","out",":","the","normalized","nd","volume"]
code: def __itensity_normalize_one_volume__(self, volume):
      """
      normalize the itensity of an nd volume based on the mean and std of nonzeor region
      inputs:
          volume: the input nd volume
      outputs:
          out: the normalized nd volume
      """
  
      pixels = volume[volume > 0]
      mean = pixels.mean()
      std = pixels.std()
      out = (volume - mean) / std
      out_random = np.random.normal(0, 1, size=volume.shape)
      out[volume == 0] = out_random[volume == 0]
      return out
  
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 7 for calling the BrainS18Dataset obj's underlying member method code with example usage: obj.__itensity_normalize_one_volume__(volume) and returns: out