class_docstr: SVM trainer that takes care of training (using k-fold cross validation),
  and evaluating the SVMs
class_docstr_tok: ["SVM","trainer","that","takes","care","of","training","(","using","k-fold","cross","validation",")",",","and","evaluating","the","SVMs"]
docstr: Normalize the features.
docstr_tok: ["Normalize","the","features","."]
code: def _normalize_features(self, features):
      """
      Normalize the features.
      """
      feats_norm = np.linalg.norm(features, axis=1)
      features = features / (feats_norm + 1e-5)[:, np.newaxis]
      return features
  
imports: import logging
  import pickle
  import threading
  import numpy
  from iopath.common.file_io import g_pathmgr
  from sklearn.model_selection import cross_val_score
  from sklearn.svm import LinearSVC
  from vissl.utils.io import load_file, save_file
  from vissl.utils.svm_utils.evaluate import get_precision_recall
label_desc: Use image node_id 4 for calling the SVMTrainer obj's underlying member method code with example usage: obj._normalize_features(features) and returns: features