class_docstr: Metric value at a given step of training across multiple executions.
  
  If the model is trained multiple
  times (multiple executions), KerasTuner records the value of each
  metric at each training step. These values are aggregated
  over multiple executions into a list where each value corresponds
  to one execution.
  
  Args:
      value: Float or a list of floats. The evaluated metric values.
      step: Int. The step of the evaluation, for example, the epoch number.
class_docstr_tok: ["Metric","value","at","a","given","step","of","training","across","multiple","executions",".","If","the","model","is","trained","multiple","times","(","multiple","executions",")",",","KerasTuner","records","the","value","of","each","metric","at","each","training","step",".","These","values","are","aggregated","over","multiple","executions","into","a","list","where","each","value","corresponds","to","one","execution",".","Args",":","value",":","Float","or","a","list","of","floats",".","The","evaluated","metric","values",".","step",":","Int",".","The","step","of","the","evaluation",",","for","example",",","the","epoch","number","."]
code: def mean(self):
      return np.mean(self.value)
  
imports: import inspect
  import numpy
  import six
  from keras_tuner import protos
  from keras_tuner.api_export import keras_tuner_export
  from keras_tuner.backend import keras
label_desc: Use image node_id 3 for calling the MetricObservation obj's underlying member method code with example usage: obj.mean() and returns: np