class_docstr: The `InteractiveLogger` class provides logging facilities
  for the console standard output. The logger shows
  a progress bar during training and evaluation flows and
  interactively display metric results as soon as they
  become available. The logger writes metric results after
  each training epoch, evaluation experience and at the
  end of the entire evaluation stream.
  
  .. note::
      To avoid an excessive amount of printed lines,
      this logger will **not** print results after
      each iteration. If the user is monitoring
      metrics which emit results after each minibatch
      (e.g., `MinibatchAccuracy`), only the last recorded
      value of such metrics will be reported at the end
      of the epoch.
  
  .. note::
      Since this logger works on the standard output,
      metrics producing images or more complex visualizations
      will be converted to a textual format suitable for
      console printing. You may want to add more loggers
      to your `EvaluationPlugin` to better support
      different formats.
code: def __setstate__(self, state):
      state["_pbar"] = None
      super().__setstate__(state)
  
imports: import sys
  from typing import List, TYPE_CHECKING
  from avalanche.core import SupervisedPlugin
  from avalanche.evaluation.metric_results import MetricValue
  from avalanche.logging import TextLogger
  from avalanche.benchmarks.scenarios import OnlineCLExperience
  from tqdm import tqdm
label_desc: Use image node_id 13 for calling the InteractiveLogger obj's underlying member method code with example usage: obj.__setstate__(state) without return types