ignite/contrib/handlers/mlflow_logger.py [114:226]:
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    def _create_output_handler(self, *args: Any, **kwargs: Any) -> "OutputHandler":
        return OutputHandler(*args, **kwargs)

    def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> "OptimizerParamsHandler":
        return OptimizerParamsHandler(*args, **kwargs)


class OutputHandler(BaseOutputHandler):
    """Helper handler to log engine's output and/or metrics.

    Args:
        tag: common title for all produced plots. For example, 'training'
        metric_names: list of metric names to plot or a string "all" to plot all available
            metrics.
        output_transform: output transform function to prepare `engine.state.output` as a number.
            For example, `output_transform = lambda output: output`
            This function can also return a dictionary, e.g `{'loss': loss1, 'another_loss': loss2}` to label the plot
            with corresponding keys.
        global_step_transform: global step transform function to output a desired global step.
            Input of the function is `(engine, event_name)`. Output of function should be an integer.
            Default is None, global_step based on attached engine. If provided,
            uses function output as global_step. To setup global step from another engine, please use
            :meth:`~ignite.contrib.handlers.mlflow_logger.global_step_from_engine`.
        state_attributes: list of attributes of the ``trainer.state`` to plot.

    Examples:
        .. code-block:: python

            from ignite.contrib.handlers.mlflow_logger import *

            # Create a logger
            mlflow_logger = MLflowLogger()

            # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after
            # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch
            # of the `trainer`:
            mlflow_logger.attach(
                evaluator,
                log_handler=OutputHandler(
                    tag="validation",
                    metric_names=["nll", "accuracy"],
                    global_step_transform=global_step_from_engine(trainer)
                ),
                event_name=Events.EPOCH_COMPLETED
            )
            # or equivalently
            mlflow_logger.attach_output_handler(
                evaluator,
                event_name=Events.EPOCH_COMPLETED,
                tag="validation",
                metric_names=["nll", "accuracy"],
                global_step_transform=global_step_from_engine(trainer)
            )

        Another example, where model is evaluated every 500 iterations:

        .. code-block:: python

            from ignite.contrib.handlers.mlflow_logger import *

            @trainer.on(Events.ITERATION_COMPLETED(every=500))
            def evaluate(engine):
                evaluator.run(validation_set, max_epochs=1)

            mlflow_logger = MLflowLogger()

            def global_step_transform(*args, **kwargs):
                return trainer.state.iteration

            # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after
            # every 500 iterations. Since evaluator engine does not have access to the training iteration, we
            # provide a global_step_transform to return the trainer.state.iteration for the global_step, each time
            # evaluator metrics are plotted on MLflow.

            mlflow_logger.attach_output_handler(
                evaluator,
                event_name=Events.EPOCH_COMPLETED,
                tag="validation",
                metrics=["nll", "accuracy"],
                global_step_transform=global_step_transform
            )

        Another example where the State Attributes ``trainer.state.alpha`` and ``trainer.state.beta``
        are also logged along with the NLL and Accuracy after each iteration:

        .. code-block:: python

            mlflow_logger.attach_output_handler(
                trainer,
                event_name=Events.ITERATION_COMPLETED,
                tag="training",
                metrics=["nll", "accuracy"],
                state_attributes=["alpha", "beta"],
            )

        Example of `global_step_transform`:

        .. code-block:: python

            def global_step_transform(engine, event_name):
                return engine.state.get_event_attrib_value(event_name)

    ..  versionchanged:: 0.5.0
        accepts an optional list of `state_attributes`
    """

    def __init__(
        self,
        tag: str,
        metric_names: Optional[Union[str, List[str]]] = None,
        output_transform: Optional[Callable] = None,
        global_step_transform: Optional[Callable] = None,
        state_attributes: Optional[List[str]] = None,
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ignite/contrib/handlers/neptune_logger.py [206:334]:
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    def _create_output_handler(self, *args: Any, **kwargs: Any) -> "OutputHandler":
        return OutputHandler(*args, **kwargs)

    def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> "OptimizerParamsHandler":
        return OptimizerParamsHandler(*args, **kwargs)


class OutputHandler(BaseOutputHandler):
    """Helper handler to log engine's output and/or metrics

    Args:
        tag: common title for all produced plots. For example, "training"
        metric_names: list of metric names to plot or a string "all" to plot all available
            metrics.
        output_transform: output transform function to prepare `engine.state.output` as a number.
            For example, `output_transform = lambda output: output`
            This function can also return a dictionary, e.g `{"loss": loss1, "another_loss": loss2}` to label the plot
            with corresponding keys.
        global_step_transform: global step transform function to output a desired global step.
            Input of the function is `(engine, event_name)`. Output of function should be an integer.
            Default is None, global_step based on attached engine. If provided,
            uses function output as global_step. To setup global step from another engine, please use
            :meth:`~ignite.contrib.handlers.neptune_logger.global_step_from_engine`.
        state_attributes: list of attributes of the ``trainer.state`` to plot.

    Examples:
        .. code-block:: python

            from ignite.contrib.handlers.neptune_logger import *

            # Create a logger
            # We are using the api_token for the anonymous user neptuner but you can use your own.

            npt_logger = NeptuneLogger(
                api_token="ANONYMOUS",
                project_name="shared/pytorch-ignite-integration",
                experiment_name="cnn-mnist", # Optional,
                params={"max_epochs": 10}, # Optional,
                tags=["pytorch-ignite","minst"] # Optional
            )

            # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after
            # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch
            # of the `trainer`:
            npt_logger.attach(
                evaluator,
                log_handler=OutputHandler(
                    tag="validation",
                    metric_names=["nll", "accuracy"],
                    global_step_transform=global_step_from_engine(trainer)
                ),
                event_name=Events.EPOCH_COMPLETED
            )
            # or equivalently
            npt_logger.attach_output_handler(
                evaluator,
                event_name=Events.EPOCH_COMPLETED,
                tag="validation",
                metric_names=["nll", "accuracy"],
                global_step_transform=global_step_from_engine(trainer)
            )

        Another example, where model is evaluated every 500 iterations:

        .. code-block:: python

            from ignite.contrib.handlers.neptune_logger import *

            @trainer.on(Events.ITERATION_COMPLETED(every=500))
            def evaluate(engine):
                evaluator.run(validation_set, max_epochs=1)

            # We are using the api_token for the anonymous user neptuner but you can use your own.

            npt_logger = NeptuneLogger(
                api_token="ANONYMOUS",
                project_name="shared/pytorch-ignite-integration",
                experiment_name="cnn-mnist", # Optional,
                params={"max_epochs": 10}, # Optional,
                tags=["pytorch-ignite", "minst"] # Optional
            )

            def global_step_transform(*args, **kwargs):
                return trainer.state.iteration

            # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after
            # every 500 iterations. Since evaluator engine does not have access to the training iteration, we
            # provide a global_step_transform to return the trainer.state.iteration for the global_step, each time
            # evaluator metrics are plotted on NeptuneML.

            npt_logger.attach_output_handler(
                evaluator,
                event_name=Events.EPOCH_COMPLETED,
                tag="validation",
                metrics=["nll", "accuracy"],
                global_step_transform=global_step_transform
            )

        Another example where the State Attributes ``trainer.state.alpha`` and ``trainer.state.beta``
        are also logged along with the NLL and Accuracy after each iteration:

        .. code-block:: python

            npt_logger.attach_output_handler(
                trainer,
                event_name=Events.ITERATION_COMPLETED,
                tag="training",
                metrics=["nll", "accuracy"],
                state_attributes=["alpha", "beta"],
            )

        Example of `global_step_transform`:

        .. code-block:: python

            def global_step_transform(engine, event_name):
                return engine.state.get_event_attrib_value(event_name)

    ..  versionchanged:: 0.5.0
        accepts an optional list of `state_attributes`
    """

    def __init__(
        self,
        tag: str,
        metric_names: Optional[Union[str, List[str]]] = None,
        output_transform: Optional[Callable] = None,
        global_step_transform: Optional[Callable] = None,
        state_attributes: Optional[List[str]] = None,
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