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.
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code: def __init__(self, name, metric_function, running=True, step_size=50):
      super(EpochLambda, self).__init__(name)
      self._step = metric_function
      self._final = metric_function
      self._step_size = step_size
      self._result = 0.0
  
      if not running:
          self._step = lambda y_pred, y_true: None
  
imports: import torchbearer
  from torchbearer.bases import Metric
  from .metrics import AdvancedMetric
  import torch
label_desc: Use image node_id 1 to create a new EpochLambda object from inherited base classes: AdvancedMetric with example: obj = EpochLambda(name, metric_function, running, step_size)