code: def run(
      self,
      network_pkl,
      run_dir=None,
      data_dir=None,
      dataset_args=None,
      mirror_augment=None,
      num_gpus=1,
      tf_config=None,
      log_results=True,
      Gs_kwargs=dict(is_validation=True),
  ):
      self._reset(
          network_pkl=network_pkl,
          run_dir=run_dir,
          data_dir=data_dir,
          dataset_args=dataset_args,
          mirror_augment=mirror_augment,
      )
      time_begin = time.time()
      with tf.Graph().as_default(), tflib.create_session(
          tf_config
      ).as_default():  # pylint: disable=not-context-manager
          self._report_progress(0, 1)
          _G, _D, Gs = misc.load_pkl(self._network_pkl)
          self._evaluate(Gs, Gs_kwargs=Gs_kwargs, num_gpus=num_gpus)
          self._report_progress(1, 1)
      self._eval_time = (
          time.time() - time_begin
      )  # pylint: disable=attribute-defined-outside-init
  
      if log_results:
          if run_dir is not None:
              log_file = os.path.join(
                  run_dir, "metric-%s.txt" % self.name
              )
              with dnnlib.util.Logger(log_file, "a"):
                  print(self.get_result_str().strip())
          else:
              print(self.get_result_str().strip())
  
imports: import os
  import time
  import hashlib
  import numpy
  import tensorflow
  import dnnlib
  import dnnlib.tflib
  from training import misc
  from training import dataset
label_desc: Use image node_id 5 for calling the MetricBase obj's underlying member method code with example usage: obj.run(network_pkl, run_dir, data_dir, dataset_args, mirror_augment, num_gpus, tf_config, log_results, Gs_kwargs) without return types