class_docstr: Callback for auto-logging metrics and parameters.
class_docstr_tok: ["Callback","for","auto-logging","metrics","and","parameters","."]
docstr: Logs accuracy and other relevant metrics on the testing end
  
  :param trainer: pytorch lightning trainer instance
  :param pl_module: pytorch lightning base module
docstr_tok: ["Logs","accuracy","and","other","relevant","metrics","on","the","testing","end",":","param","trainer",":","pytorch","lightning","trainer","instance",":","param","pl_module",":","pytorch","lightning","base","module"]
code: def on_test_end(self, trainer, pl_module):
      """
      Logs accuracy and other relevant metrics on the testing end
  
      :param trainer: pytorch lightning trainer instance
      :param pl_module: pytorch lightning base module
      """
      self.client.set_tags(self.run_id, {"Mode": "testing"})
      self.client.flush(synchronous=True)
  
      self.metrics_logger.record_metrics(
          {
              key: float(value)
              for key, value in trainer.callback_metrics.items()
          }
      )
      self.metrics_logger.flush()
  
imports: import logging
  import os
  import tempfile
  import warnings
  from packaging.version import Version
  import mlflow.pytorch
  from mlflow.exceptions import MlflowException
  from mlflow.ml_package_versions import _ML_PACKAGE_VERSIONS
  from mlflow.pytorch import _pytorch_autolog
  from mlflow.utils.autologging_utils import BatchMetricsLogger, ExceptionSafeAbstractClass, MlflowAutologgingQueueingClient, get_autologging_config
  import pytorch_lightning
  from pytorch_lightning.utilities import rank_zero_only
label_desc: Use image node_id 7 for calling the __MLflowPLCallback obj's underlying member method code with example usage: obj.on_test_end(trainer, pl_module) without return types