class_docstr: A metric which aggregates batches of results and presents a method to periodically process these into a value.
  
  .. note::
  
     Running metrics only provide output during training.
  
  Args:
      name (str): The name of the metric.
      batch_size (int): The size of the deque to store of previous results.
      step_size (int): The number of iterations between aggregations.
class_docstr_tok: ["A","metric","which","aggregates","batches","of","results","and","presents","a","method","to","periodically","process","these","into","a","value",".","..","note",":",":","Running","metrics","only","provide","output","during","training",".","Args",":","name","(","str",")",":","The","name","of","the","metric",".","batch_size","(","int",")",":","The","size","of","the","deque","to","store","of","previous","results",".","step_size","(","int",")",":","The","number","of","iterations","between","aggregations","."]
docstr: Process the metric for a single train step.
  
  Args:
      args: The output of some :class:`.Metric`
  
  Returns:
      The metric value.
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code: def _process_train(self, *args):
      """Process the metric for a single train step.
  
      Args:
          args: The output of some :class:`.Metric`
  
      Returns:
          The metric value.
      """
      raise NotImplementedError
  
imports: from collections import deque
  import torch
  from torchbearer.bases import Metric
  from .metrics import AdvancedMetric
label_desc: Use image node_id 2 for calling the RunningMetric obj's underlying member method code with example usage: obj._process_train() without return types