class_docstr: The layer that calculates the confusion matrix for classification
  results.
  The columns correspond to the network response, the rows - to 
  the correct labels. Each element of the matrix contains the number 
  of objects that belong to the "row" class and were classified 
  as the "column" class.
  
  :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 the number of classes and should be greater than 1
  
  (2) a blob with the correct class labels
      The dimensions should be the same as for the first input
  
  .. rubric:: Layer outputs:
  
  (1) the confusion matrix.
      The dimensions:
  
      - **BatchLength**, **BatchWidth**, **ListSize**, **Depth**, **Channels** are 1
      - **Height** and **Width** are equal to the input **Channels**
docstr: Resets the confusion matrix values. The dimensions:
  
  - **BatchLength**, **BatchWidth**, **ListSize**, **Depth**, **Channels** are 1
  - **Height** and **Width** are equal to the input **Channels**
docstr_tok: ["Resets","the","confusion","matrix","values",".","The","dimensions",":","-","*","*","BatchLength","*","*",",","*","*","BatchWidth","*","*",",","*","*","ListSize","*","*",",","*","*","Depth","*","*",",","*","*","Channels","*","*","are","1","-","*","*","Height","*","*","and","*","*","Width","*","*","are","equal","to","the","input","*","*","Channels","*","*"]
code: def reset_matrix(self):
      """Resets the confusion matrix values. The dimensions:
  
      - **BatchLength**, **BatchWidth**, **ListSize**, **Depth**, **Channels** are 1
      - **Height** and **Width** are equal to the input **Channels**
      """
      return self._internal.reset_matrix()
  
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 5 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.reset_matrix() and returns: self