class_docstr: MLflow logger to manage logging of experiments parameters, metrics update, models log and other information. The
  logger will log all run into the same experiment.
  
  Args:
      experiment_name (Optional[str]): The name of the experiment. The name is case-sensitive. An `experiment_id` must
          not be passed if this is passed.
      experiment_id (Optional[str]): The id of the experiment. An `experiment_name` must not be passed if this is
          passed.
      run_id (Optional[str]): The id of the run. An experiment name/id must not be passed if this is passed.
      tracking_uri (Optional[str]): Either the URI tracking path (for server tracking) of the absolute path to
          the directory to save the files (for file store). For example: ``http://<ip address>:<port>``
          (remote server) or ``/home/<user>/mlflow-server`` (local server).
          If None, will use the default MLflow file tracking URI ``"./mlruns"``.
      batch_granularity (bool): Whether to also output the result of each batch in addition to the epochs.
          (Default value = False)
      terminate_on_end (bool): Wheter to end the run at the end of the training or testing. (Default value = True)
  
  Example:
      Using file store::
  
          mlflow_logger = MLFlowLogger(experiment_name="experiment", tracking_uri="/absolute/path/to/directory")
          mlflow_logger.log_config_params(config_params=cfg_dict) # logging the config dictionary
  
          # our Poutyne model bundle
          model_bundle = ModelBundle.from_network(directory=saving_directory, network=network, optimizer=optimizer,
                                                  loss_function=cross_entropy_loss, batch_metrics=[accuracy],
                                                  device=device)
  
          # Using the MLflow logger callback during training
          model_bundle.train(train_generator=train_loader, valid_generator=valid_loader, epochs=1,
                             seed=42, callbacks=[mlflow_logger])
  
      Using server tracking::
  
          mlflow_logger = MLFlowLogger(experiment_name="experiment", tracking_uri="http://IP_ADDRESS:PORT")
          mlflow_logger.log_config_params(config_params=cfg_dict) # logging the config dictionary
  
          # our Poutyne model bundle
          model_bundle = ModelBundle.from_network(directory=saving_directory, network=network, optimizer=optimizer,
                                                  loss_function=cross_entropy_loss, batch_metrics=[accuracy],
                                                  device=device)
  
          # Using the MLflow logger callback during training
          model_bundle.train(train_generator=train_loader, valid_generator=valid_loader, epochs=1,
                             seed=42, callbacks=[mlflow_logger])
class_docstr_tok: ["MLflow","logger","to","manage","logging","of","experiments","parameters",",","metrics","update",",","models","log","and","other","information",".","The","logger","will","log","all","run","into","the","same","experiment",".","Args",":","experiment_name","(","Optional","[","str","]",")",":","The","name","of","the","experiment",".","The","name","is","case-sensitive",".","An","`","experiment_id","`","must","not","be","passed","if","this","is","passed",".","experiment_id","(","Optional","[","str","]",")",":","The","id","of","the","experiment",".","An","`","experiment_name","`","must","not","be","passed","if","this","is","passed",".","run_id","(","Optional","[","str","]",")",":","The","id","of","the","run",".","An","experiment","name\/id","must","not","be","passed","if","this","is","passed",".","tracking_uri","(","Optional","[","str","]",")",":","Either","the","URI","tracking","path","(","for","server","tracking",")","of","the","absolute","path","to","the","directory","to","save","the","files","(","for","file","store",")",".","For","example",":","``","http",":","\/\/","<","ip","address",">",":","<","port",">","``","(","remote","server",")","or","``","\/home\/","<","user",">","\/mlflow-server","``","(","local","server",")",".","If","None",",","will","use","the","default","MLflow","file","tracking","URI","``","``",".\/mlruns","''","``",".","batch_granularity","(","bool",")",":","Whether","to","also","output","the","result","of","each","batch","in","addition","to","the","epochs",".","(","Default","value","=","False",")","terminate_on_end","(","bool",")",":","Wheter","to","end","the","run","at","the","end","of","the","training","or","testing",".","(","Default","value","=","True",")","Example",":","Using","file","store",":",":","mlflow_logger","=","MLFlowLogger","(","experiment_name=","''","experiment","''",",","tracking_uri=","''","\/absolute\/path\/to\/directory","''",")","mlflow_logger.log_config_params","(","config_params=cfg_dict",")","#","logging","the","config","dictionary","#","our","Poutyne","model","bundle","model_bundle","=","ModelBundle.from_network","(","directory=saving_directory",",","network=network",",","optimizer=optimizer",",","loss_function=cross_entropy_loss",",","batch_metrics=","[","accuracy","]",",","device=device",")","#","Using","the","MLflow","logger","callback","during","training","model_bundle.train","(","train_generator=train_loader",",","valid_generator=valid_loader",",","epochs=1",",","seed=42",",","callbacks=","[","mlflow_logger","]",")","Using","server","tracking",":",":","mlflow_logger","=","MLFlowLogger","(","experiment_name=","''","experiment","''",",","tracking_uri=","''","http",":","\/\/IP_ADDRESS",":","PORT","''",")","mlflow_logger.log_config_params","(","config_params=cfg_dict",")","#","logging","the","config","dictionary","#","our","Poutyne","model","bundle","model_bundle","=","ModelBundle.from_network","(","directory=saving_directory",",","network=network",",","optimizer=optimizer",",","loss_function=cross_entropy_loss",",","batch_metrics=","[","accuracy","]",",","device=device",")","#","Using","the","MLflow","logger","callback","during","training","model_bundle.train","(","train_generator=train_loader",",","valid_generator=valid_loader",",","epochs=1",",","seed=42",",","callbacks=","[","mlflow_logger","]",")"]
docstr: Log the test results.
docstr_tok: ["Log","the","test","results","."]
code: def on_test_end(self, logs: Dict):
      """
      Log the test results.
      """
      if len(logs) > 0:  # To manage failure of the test loop.
          self._on_test_end_write(logs)
          self._status = "FINISHED"
  
      self._status_handling()
  
imports: import os
  import warnings
  from typing import Any, Dict, Mapping, Optional, Sequence, Union
  from poutyne.framework.callbacks.logger import Logger
label_desc: Use image node_id 13 for calling the MLFlowLogger obj's underlying member method code with example usage: obj.on_test_end(logs) without return types