class_docstr: 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.
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code: 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,
  ) -> None:
      super(OutputHandler, self).__init__(
          tag,
          metric_names,
          output_transform,
          global_step_transform,
          state_attributes,
      )
  
imports: from typing import Any, Callable, Dict, List, Optional, Union
  from aim import Run
  from aim.ext.resource.configs import DEFAULT_SYSTEM_TRACKING_INT
label_desc: Use image node_id 1 to create a new OutputHandler object from inherited base classes: BaseOutputHandler with example: obj = OutputHandler(tag, metric_names, output_transform, global_step_transform, state_attributes)