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: Costs values for which SVM training is done. We take costs values
  specified in the costs_list input in the config file. Additionally,
  costs specified to be the powers of a base value are also added
  (assuming the base value is > 0).
docstr_tok: ["Costs","values","for","which","SVM","training","is","done",".","We","take","costs","values","specified","in","the","costs_list","input","in","the","config","file",".","Additionally",",","costs","specified","to","be","the","powers","of","a","base","value","are","also","added","(","assuming","the","base","value","is",">","0",")","."]
code: def _get_costs_list(self):
      """
      Costs values for which SVM training is done. We take costs values
      specified in the costs_list input in the config file. Additionally,
      costs specified to be the powers of a base value are also added
      (assuming the base value is > 0).
      """
      costs_list = self.config["costs"]["costs_list"]
      # we append more costs to the output based on power function
      if self.config["costs"]["base"] > 0.0:
          base = self.config["costs"]["base"]
          start_num, end_num = self.config["costs"]["power_range"]
          for num in range(start_num, end_num):
              costs_list.append(base**num)
      self.costs_list = costs_list
      logging.info("Training SVM for costs: {}".format(costs_list))
      return costs_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 5 for calling the SVMTrainer obj's underlying member method code with example usage: obj._get_costs_list() and returns: costs_list