class_docstr: Callback for auto-logging metrics and parameters.
class_docstr_tok: ["Callback","for","auto-logging","metrics","and","parameters","."]
docstr: Logs the model checkpoint into mlflow - models folder on the training end
  
  :param trainer: pytorch lightning trainer instance
  :param pl_module: pytorch lightning base module
docstr_tok: ["Logs","the","model","checkpoint","into","mlflow","-","models","folder","on","the","training","end",":","param","trainer",":","pytorch","lightning","trainer","instance",":","param","pl_module",":","pytorch","lightning","base","module"]
code: def on_train_end(self, trainer, pl_module):
      """
      Logs the model checkpoint into mlflow - models folder on the training end
  
      :param trainer: pytorch lightning trainer instance
      :param pl_module: pytorch lightning base module
      """
      # manually flush any remaining metadata from training
      self.metrics_logger.flush()
      self.client.flush(synchronous=True)
  
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 6 for calling the __MLflowPLCallback obj's underlying member method code with example usage: obj.on_train_end(trainer, pl_module) without return types