text: import numbers
  import warnings
  from abc import ABCMeta, abstractmethod
  from typing import Any, Callable, Dict, List, Optional, Sequence, Union
  
  import torch
  import torch.nn as nn
  from torch.optim import Optimizer
  
  from ignite.engine import Engine, Events, EventsList, State
  from ignite.engine.events import CallableEventWithFilter, RemovableEventHandle
  
  
  class BaseHandler(metaclass=ABCMeta):
      @abstractmethod
      def __call__(self, engine: Engine, logger: Any, event_name: Union[str, Events]) -> None:
          pass
  
  
  class BaseOptimizerParamsHandler(BaseHandler):
      """
      Base handler for logging optimizer parameters
      """
  
      def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None):
          if not (
              isinstance(optimizer, Optimizer)
              or (hasattr(optimizer, "param_groups") and isinstance(optimizer.param_groups, Sequence))
          ):
              raise TypeError(
                  "Argument optimizer should be torch.optim.Optimizer or has attribute 'param_groups' as list/tuple, "
                  f"but given {type(optimizer)}"
              )
  
          self.optimizer = optimizer
          self.param_name = param_name
          self.tag = tag
  
  
  class BaseOutputHandler(BaseHandler):
      """
      Helper handler to log engine's output and/or metrics
      """
  
      def __init__(
          self,
          tag: str,
          metric_names: Optional[Union[str, List[str]]] = None,
          output_transform: Optional[Callable] = None,
          global_step_transform: Optional[Callable] = None,
      ):
  
          if metric_names is not None:
              if not (isinstance(metric_names, list) or (isinstance(metric_names, str) and metric_names == "all")):
                  raise TypeError(
                      f"metric_names should be either a list or equal 'all', got {type(metric_names)} instead."
                  )
  
          if output_transform is not None and not callable(output_transform):
              raise TypeError(f"output_transform should be a function, got {type(output_transform)} instead.")
  
          if output_transform is None and metric_names is None:
              raise ValueError("Either metric_names or output_transform should be defined")
  
          if global_step_transform is not None and not callable(global_step_transform):
              raise TypeError(f"global_step_transform should be a function, got {type(global_step_transform)} instead.")
  
          if global_step_transform is None:
  
              def global_step_transform(engine: Engine, event_name: Union[str, Events]) -> int:
                  return engine.state.get_event_attrib_value(event_name)
  
          self.tag = tag
          self.metric_names = metric_names
          self.output_transform = output_transform
          self.global_step_transform = global_step_transform
  
      def _setup_output_metrics(self, engine: Engine) -> Dict[str, Any]:
          """Helper method to setup metrics to log
          """
          metrics = {}
          if self.metric_names is not None:
              if isinstance(self.metric_names, str) and self.metric_names == "all":
                  metrics = engine.state.metrics
              else:
                  for name in self.metric_names:
                      if name not in engine.state.metrics:
                          warnings.warn(
                              f"Provided metric name '{name}' is missing "
                              f"in engine's state metrics: {list(engine.state.metrics.keys())}"
                          )
                          continue
                      metrics[name] = engine.state.metrics[name]
  
          if self.output_transform is not None:
              output_dict = self.output_transform(engine.state.output)
  
              if not isinstance(output_dict, dict):
                  output_dict = {"output": output_dict}
  
              metrics.update({name: value for name, value in output_dict.items()})
          return metrics
  
  
  class BaseWeightsScalarHandler(BaseHandler):
      """
      Helper handler to log model's weights as scalars.
      """
  
      def __init__(self, model: nn.Module, reduction: Callable = torch.norm, tag: Optional[str] = None):
          if not isinstance(model, torch.nn.Module):
              raise TypeError(f"Argument model should be of type torch.nn.Module, but given {type(model)}")
  
          if not callable(reduction):
              raise TypeError(f"Argument reduction should be callable, but given {type(reduction)}")
  
          def _is_0D_tensor(t: torch.Tensor) -> bool:
              return isinstance(t, torch.Tensor) and t.ndimension() == 0
  
          # Test reduction function on a tensor
          o = reduction(torch.ones(4, 2))
          if not (isinstance(o, numbers.Number) or _is_0D_tensor(o)):
              raise TypeError(f"Output of the reduction function should be a scalar, but got {type(o)}")
  
          self.model = model
          self.reduction = reduction
          self.tag = tag
  
  
  class BaseWeightsHistHandler(BaseHandler):
      """
      Helper handler to log model's weights as histograms.
      """
  
      def __init__(self, model: nn.Module, tag: Optional[str] = None):
          if not isinstance(model, torch.nn.Module):
              raise TypeError(f"Argument model should be of type torch.nn.Module, but given {type(model)}")
  
          self.model = model
          self.tag = tag
  
  
  class BaseLogger(metaclass=ABCMeta):
      """
      Base logger handler. See implementations: TensorboardLogger, VisdomLogger, PolyaxonLogger, MLflowLogger, ...
  
      """
  
      def attach(
          self, engine: Engine, log_handler: Callable, event_name: Union[str, Events, CallableEventWithFilter, EventsList]
      ) -> RemovableEventHandle:
          """Attach the logger to the engine and execute `log_handler` function at `event_name` events.
  
          Args:
              engine: engine object.
              log_handler: a logging handler to execute
              event_name: event to attach the logging handler to. Valid events are from
                  :class:`~ignite.engine.events.Events` or :class:`~ignite.engine.events.EventsList` or any `event_name`
                  added by :meth:`~ignite.engine.engine.Engine.register_events`.
  
          Returns:
              :class:`~ignite.engine.events.RemovableEventHandle`, which can be used to remove the handler.
          """
          if isinstance(event_name, EventsList):
              for name in event_name:
                  if name not in State.event_to_attr:
                      raise RuntimeError(f"Unknown event name '{name}'")
                  engine.add_event_handler(name, log_handler, self, name)
  
              return RemovableEventHandle(event_name, log_handler, engine)
  
          else:
  
              if event_name not in State.event_to_attr:
                  raise RuntimeError(f"Unknown event name '{event_name}'")
  
              return engine.add_event_handler(event_name, log_handler, self, event_name)
  
      def attach_output_handler(self, engine: Engine, event_name: Any, *args: Any, **kwargs: Any) -> RemovableEventHandle:
          """Shortcut method to attach `OutputHandler` to the logger.
  
          Args:
              engine: engine object.
              event_name: event to attach the logging handler to. Valid events are from
                  :class:`~ignite.engine.events.Events` or any `event_name` added by
                  :meth:`~ignite.engine.engine.Engine.register_events`.
              args: args to initialize `OutputHandler`
              kwargs: kwargs to initialize `OutputHandler`
  
          Returns:
              :class:`~ignite.engine.events.RemovableEventHandle`, which can be used to remove the handler.
          """
          return self.attach(engine, self._create_output_handler(*args, **kwargs), event_name=event_name)
  
      def attach_opt_params_handler(
          self, engine: Engine, event_name: Any, *args: Any, **kwargs: Any
      ) -> RemovableEventHandle:
          """Shortcut method to attach `OptimizerParamsHandler` to the logger.
  
          Args:
              engine: engine object.
              event_name: event to attach the logging handler to. Valid events are from
                  :class:`~ignite.engine.events.Events` or any `event_name` added by
                  :meth:`~ignite.engine.engine.Engine.register_events`.
              args: args to initialize `OptimizerParamsHandler`
              kwargs: kwargs to initialize `OptimizerParamsHandler`
  
          Returns:
              :class:`~ignite.engine.events.RemovableEventHandle`, which can be used to remove the handler.
  
          .. versionchanged:: 0.4.3
              Added missing return statement.
          """
          return self.attach(engine, self._create_opt_params_handler(*args, **kwargs), event_name=event_name)
  
      @abstractmethod
      def _create_output_handler(self, engine: Engine, *args: Any, **kwargs: Any) -> Callable:
          pass
  
      @abstractmethod
      def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> Callable:
          pass
  
      def __enter__(self) -> "BaseLogger":
          return self
  
      def __exit__(self, type: Any, value: Any, traceback: Any) -> None:
          self.close()
  
      def close(self) -> None:
          pass
  