code: def preprocessFeature(self):
      if self.features.shape[1] > 200:
          U, S, VT = la.svd(self.features)
          Ud = U[:, 0:200]
          Sd = S[0:200]
          self.features = np.array(Ud) * Sd.reshape(200)
  
file_path: OpenNE/src/openne/tadw.py
imports: from __future__ import print_function
  import math
  import numpy
  from numpy import linalg
  from sklearn.preprocessing import normalize
  from .gcn.utils import *
label_desc: Use image node_id 5 for calling the TADW obj's underlying member method code with example usage: obj.preprocessFeature() without return types