docstr: Calculate the column wise standard deviations of a sparse matrix.
docstr_tok: ["Calculate","the","column","wise","standard","deviations","of","a","sparse","matrix","."]
code: def sparse_std(X, X_mean):
      """
      Calculate the column wise standard deviations of a sparse matrix.
      """
      X_copy = X.copy()
      X_copy.data **= 2  # square non zero elements
      E_x_squared = np.array(X_copy.mean(axis=0)).ravel()
      Xsd = np.sqrt(E_x_squared - X_mean**2)
      return Xsd
  
imports: import os
  import pickle
  import sys
  import time
  import numpy
  import scipy.sparse
  from sklearn.datasets import load_svmlight_file
  import torch
  from torch.utils.data import DataLoader, Dataset
  from torch.utils.data.dataloader import _SingleProcessDataLoaderIter, _MultiProcessingDataLoaderIter, _utils
  from .None import constants
  from .None import syssettings
label_desc: Use image node_id 11 for calling the PrepareData obj's underlying member method code with example usage: obj.sparse_std(X, X_mean) and returns: Xsd