class_docstr: The layer that calculates the number of objects classified
  correctly for either class in a binary classification scenario.
  Using these statistics, you can easily calculate the precision 
  and recall for the trained network.
  
  :param input_layers: The input layers to be connected. 
      The integer in each tuple specifies the number of the output.
  :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) the network response.
      The dimensions:
  
      - **BatchLength** * **BatchWidth** * **ListSize** is the number of objects classified
      - **Height**, **Width**, **Depth**, **Channels** are 1
  
  (2) the correct class labels (1 or -1).
      The dimensions are the same as for the first input.
  
  .. rubric:: Layer outputs:
  
  (1) the 4-element array along the **Channels** dimension that contains:
  
      - 0 - the number of correctly classified objects in class 1
      - 1 - the total number of objects in class 1
      - 2 - the number of correctly classified objects in class -1
      - 3 - the total number of objects in class -1
docstr: Specifies if the statistics should be reset after each run.
          
code: def reset(self, reset):
      """Specifies if the statistics should be reset after each run."""
      self._internal.set_reset(bool(reset))
  
file_path: neoml/NeoML/Python/neoml/Dnn/PrecisionRecall.py
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
label_desc: Use image node_id 3 for calling the PrecisionRecall obj's underlying member method code with example usage: obj.reset(reset) without return types