class_docstr: A metric wrapper which computes the given function for concatenated values of 'y_true' and 'y_pred' each epoch.
  Can be used as a running metric which computes the function for batches of outputs with a given step size during
  training.
  
  Args:
      name (str): The name of the metric.
      metric_function (func): The function('y_pred', 'y_true') to use as the metric.
      running (bool): True if this should act as a running metric.
      step_size (int): Step size to use between calls if running=True.
class_docstr_tok: ["A","metric","wrapper","which","computes","the","given","function","for","concatenated","values","of","'y_true","'","and","'y_pred","'","each","epoch",".","Can","be","used","as","a","running","metric","which","computes","the","function","for","batches","of","outputs","with","a","given","step","size","during","training",".","Args",":","name","(","str",")",":","The","name","of","the","metric",".","metric_function","(","func",")",":","The","function","(","'y_pred","'",",","'y_true","'",")","to","use","as","the","metric",".","running","(","bool",")",":","True","if","this","should","act","as","a","running","metric",".","step_size","(","int",")",":","Step","size","to","use","between","calls","if","running=True","."]
docstr: Evaluate the function with the aggregated outputs.
  
  Returns:
      The result of the function.
docstr_tok: ["Evaluate","the","function","with","the","aggregated","outputs",".","Returns",":","The","result","of","the","function","."]
code: def process_final_train(self, *args):
      """Evaluate the function with the aggregated outputs.
  
      Returns:
          The result of the function.
      """
      return self._final(self._y_pred, self._y_true)
  
imports: import torchbearer
  from torchbearer.bases import Metric
  from .metrics import AdvancedMetric
  import torch
label_desc: Use image node_id 3 for calling the EpochLambda obj's underlying member method code with example usage: obj.process_final_train() and returns: self