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.
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docstr: Add the current metric value to the cache and call '_step' is needed.
  
  Args:
      args: The output of some :class:`.Metric`
  
  Returns:
      The current metric value.
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code: def process_train(self, *args):
      """Add the current metric value to the cache and call '_step' is needed.
  
      Args:
          args: The output of some :class:`.Metric`
  
      Returns:
          The current metric value.
      """
      self._cache.append(self._process_train(*args))
      if len(self._cache) > self._batch_size:
          self._cache.popleft()
      if self._i % self._step_size == 0:
          self._result = self._step(list(self._cache))
      self._i += 1
      return self._result
  
imports: from collections import deque
  import torch
  from torchbearer.bases import Metric
  from .metrics import AdvancedMetric
label_desc: Use image node_id 4 for calling the RunningMetric obj's underlying member method code with example usage: obj.process_train() and returns: self