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","."]
code: def __init__(self, name, batch_size=50, step_size=10):
      super(RunningMetric, self).__init__(name)
      self._batch_size = batch_size
      self._step_size = step_size
      self._cache = deque()
      self._result = {}
  
imports: from collections import deque
  import torch
  from torchbearer.bases import Metric
  from .metrics import AdvancedMetric
label_desc: Use image node_id 1 to create a new RunningMetric object from inherited base classes: AdvancedMetric with example: obj = RunningMetric(name, batch_size, step_size)