def end(self, session):
        if self.mode == tf.estimator.ModeKeys.EVAL:
            current_global_step = session.run(self.global_step_tensor)
            with open(os.path.join(self.writer.get_logdir(), "checkpoint")) as f:
                checkpoints = [ckpt for ckpt in f]
                checkpoints = [self.extract_global_step(ckpt) for ckpt in checkpoints[1:]]
                checkpoints = list(filter(lambda gs: gs < current_global_step, checkpoints))
                if len(checkpoints) > self.keep_eval_results_max_epoch:
                    checkpoint_to_delete = checkpoints[-self.keep_eval_results_max_epoch]
                    tf.logging.info("Deleting %s results at the step %d", self.mode, checkpoint_to_delete)
                    tfrecord_filespec = os.path.join(self.writer.get_logdir(),
                                                     "eval_result_step{:09d}_*.tfrecord".format(checkpoint_to_delete))
                    alignment_filespec = os.path.join(self.writer.get_logdir(),
                                                      "alignment_eval_result_step{:09d}_*.png".format(
                                                          checkpoint_to_delete))
                    mel_filespec = os.path.join(self.writer.get_logdir(),
                                                "mel_eval_result_step{:09d}_*.png".format(checkpoint_to_delete))
                    for pathname in tf.gfile.Glob([tfrecord_filespec, alignment_filespec, mel_filespec]):
                        file_io.delete_file(pathname) 

def _delete_file_if_exists(self, filespec):
    for pathname in file_io.get_matching_files(filespec):
      file_io.delete_file(pathname) 

def _delete_file_if_exists(self, filespec):
    for pathname in file_io.get_matching_files(filespec):
      file_io.delete_file(pathname) 

def _delete_file_if_exists(self, filespec):
    for pathname in file_io.get_matching_files(filespec):
      file_io.delete_file(pathname) 

def create_object_test():
  """Verifies file_io's object manipulation methods ."""
  starttime = int(round(time.time() * 1000))
  dir_name = "%s/tf_gcs_test_%s" % (FLAGS.gcs_bucket_url, starttime)
  print("Creating dir %s." % dir_name)
  file_io.create_dir(dir_name)

  # Create a file in this directory.
  file_name = "%s/test_file.txt" % dir_name
  print("Creating file %s." % file_name)
  file_io.write_string_to_file(file_name, "test file creation.")

  list_files_pattern = "%s/test_file*.txt" % dir_name
  print("Getting files matching pattern %s." % list_files_pattern)
  files_list = file_io.get_matching_files(list_files_pattern)
  print(files_list)

  assert len(files_list) == 1
  assert files_list[0] == file_name

  # Cleanup test files.
  print("Deleting file %s." % file_name)
  file_io.delete_file(file_name)

  # Delete directory.
  print("Deleting directory %s." % dir_name)
  file_io.delete_recursively(dir_name) 

def _delete_file_if_exists(self, filespec):
        for pathname in file_io.get_matching_files(filespec):
            s = os.stat(pathname)[stat.ST_MODE]
            if not (s & stat.S_IWUSR):      # skip read-only file
                continue
            file_io.delete_file(pathname) 

def _delete_file_if_exists(self, filespec):
    for pathname in file_io.get_matching_files(filespec):
      file_io.delete_file(pathname) 

def _delete_file_if_exists(self, filespec):
    for pathname in file_io.get_matching_files(filespec):
      file_io.delete_file(pathname)