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_output_dir(self, cfg_out_dir):
      odir = f"{cfg_out_dir}/{self.layer}"
      g_pathmgr.mkdirs(odir)
      logging.info(f"Output directory for SVM results: {odir}")
      return odir
  
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 2 for calling the SVMTrainer obj's underlying member method code with example usage: obj._get_output_dir(cfg_out_dir) and returns: odir