class_docstr: EpochOutputStore handler to save output prediction and target history
  after every epoch, could be useful for e.g., visualization purposes.
  
  Note:
      This can potentially lead to a memory error if the output data is
      larger than available RAM.
  
  Args:
      output_transform: a callable that is used to
          transform the :class:`~ignite.engine.engine.Engine`'s
          ``process_function``'s output , e.g., lambda x: x[0]
  
  Attributes:
      data: a list of :class:`~ignite.engine.engine.Engine` outputs,
          optionally transformed by `output_transform`.
  
  Examples:
      .. code-block:: python
  
          eos = EpochOutputStore()
          trainer = create_supervised_trainer(model, optimizer, loss)
          train_evaluator = create_supervised_evaluator(model, metrics)
          eos.attach(train_evaluator, 'output')
  
          @trainer.on(Events.EPOCH_COMPLETED)
          def log_training_results(engine):
              train_evaluator.run(train_loader)
              output = train_evaluator.state.output
              # output = [(y_pred0, y0), (y_pred1, y1), ...]
              # do something with output, e.g., plotting
  
  .. versionadded:: 0.4.5
  .. versionchanged:: 0.4.5
      `attach` now accepts an optional argument `name`
code: def __init__(self, output_transform: Callable = lambda x: x):
      self.data: List[Any] = []
      self.output_transform = output_transform
  
file_path: ignite/ignite/handlers/stores.py
imports: from typing import Any, Callable, List, Optional
  from ignite.engine import Engine, Events
label_desc: Use image node_id 1 to create a new EpochOutputStore object  with example: obj = EpochOutputStore(output_transform)