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"]
code: def _get_cls_list(self, targets):
      num_classes = targets.shape[1]
      cls_list = range(num_classes)
      if len(self.config["cls_list"]) > 0:
          cls_list = [
              int(cls_num) for cls_num in self.config["cls_list"]
          ]
      self.cls_list = cls_list
      logging.info("Training SVM for classes: {}".format(self.cls_list))
      return cls_list
  
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 6 for calling the SVMTrainer obj's underlying member method code with example usage: obj._get_cls_list(targets) and returns: cls_list