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: Reset the step counter. Does not clear the cache.
  
  Args:
      state (dict): The current model state.
docstr_tok: ["Reset","the","step","counter",".","Does","not","clear","the","cache",".","Args",":","state","(","dict",")",":","The","current","model","state","."]
code: def reset(self, state):
      """Reset the step counter. Does not clear the cache.
  
      Args:
          state (dict): The current model state.
      """
      self._i = 0
  
imports: from collections import deque
  import torch
  from torchbearer.bases import Metric
  from .metrics import AdvancedMetric
label_desc: Use image node_id 5 for calling the RunningMetric obj's underlying member method code with example usage: obj.reset(state) without return types