ignite/contrib/handlers/clearml_logger.py [191:313]:
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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.clearml_logger.global_step_from_engine`.
        state_attributes: list of attributes of the ``trainer.state`` to plot.

    Examples:
        .. code-block:: python

            from ignite.contrib.handlers.clearml_logger import *

            # Create a logger

            clearml_logger = ClearMLLogger(
                project_name="pytorch-ignite-integration",
                task_name="cnn-mnist"
            )

            # 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`:
            clearml_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
            clearml_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.clearml_logger import *

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

            # Create a logger

            clearml_logger = ClearMLLogger(
                project_name="pytorch-ignite-integration",
                task_name="cnn-mnist"
            )

            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 ClearML.

            clearml_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

            clearml_logger.attach(
                trainer,
                log_handler=OutputHandler(
                    tag="training",
                    metric_names=["nll", "accuracy"],
                    state_attributes=["alpha", "beta"],
                ),
                event_name=Events.ITERATION_COMPLETED
            )

        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[List[str]] = None,
        output_transform: Optional[Callable] = None,
        global_step_transform: Optional[Callable] = None,
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ignite/contrib/handlers/wandb_logger.py [143:273]:
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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.wandb_logger.global_step_from_engine`.
        sync: If set to False, process calls to log in a seperate thread. Default (None) uses whatever
            the default value of wandb.log.

    Examples:
        .. code-block:: python

            from ignite.contrib.handlers.wandb_logger import *

            # Create a logger. All parameters are optional. See documentation
            # on wandb.init for details.

            wandb_logger = WandBLogger(
                entity="shared",
                project="pytorch-ignite-integration",
                name="cnn-mnist",
                config={"max_epochs": 10},
                tags=["pytorch-ignite", "minst"]
            )

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

        Another example, where model is evaluated every 500 iterations:

        .. code-block:: python

            from ignite.contrib.handlers.wandb_logger import *

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

            # Create a logger. All parameters are optional. See documentation
            # on wandb.init for details.

            wandb_logger = WandBLogger(
                entity="shared",
                project="pytorch-ignite-integration",
                name="cnn-mnist",
                config={"max_epochs": 10},
                tags=["pytorch-ignite", "minst"]
            )

            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 Weights & Biases.

            wandb_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

            wandb_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[List[str]] = None,
        output_transform: Optional[Callable] = None,
        global_step_transform: Optional[Callable] = None,
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