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: Aggregate the cache to produce a single metric value.
  
  Args:
      cache (list): The current stored metric cache.
  
  Returns:
      The new metric value.
docstr_tok: ["Aggregate","the","cache","to","produce","a","single","metric","value",".","Args",":","cache","(","list",")",":","The","current","stored","metric","cache",".","Returns",":","The","new","metric","value","."]
code: def _step(self, cache):
      """Aggregate the cache to produce a single metric value.
  
      Args:
          cache (list): The current stored metric cache.
  
      Returns:
          The new 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 3 for calling the RunningMetric obj's underlying member method code with example usage: obj._step(cache) without return types