class_docstr: The layer that calculates classification accuracy, that is,
  the proportion of objects classified correctly in the set.
  
  :param input_layers: The input layers to be connected. 
      The integer in each tuple specifies the number of the output.
      If not set, the first output will be used.
  :type input_layers: list of object, tuple(object, int)
  :param reset: Specifies if the statistics should be reset with each run.
      Set to False to accumulate statistics for subsequent runs.
  :type reset: bool, default=True
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) a blob with the network response
      The dimensions:
  
      - **BatchLength** * **BatchWidth** * **ListSize** equal to the number of objects 
        that were classified
      - **Height**, **Width**, **Depth** equal to 1
      - **Channels** equal to 1 for binary classification and to the number of classes 
        if there are more than 2
  
  (2) a blob with the correct class labels
      The dimensions should be the same as for the first input
  
  .. rubric:: Layer outputs:
  
  (1) a blob with only one element, which contains the proportion of
      correctly classified objects among all objects
code: def __init__(self, input_layers, reset=True, name=None):
      if type(input_layers) is PythonWrapper.Accuracy:
          super().__init__(input_layers)
          return
  
      layers, outputs = check_input_layers(input_layers, 2)
  
      internal = PythonWrapper.Accuracy(
          str(name),
          layers[0],
          layers[1],
          int(outputs[0]),
          int(outputs[1]),
          bool(reset),
      )
      super().__init__(internal)
  
file_path: neoml/NeoML/Python/neoml/Dnn/Accuracy.py
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
label_desc: Use image node_id 1 to create a new Accuracy object from inherited base classes: Layer with example: obj = Accuracy(input_layers, reset, name)