class_docstr: Lrn layer performs local response normlization with the following formula:
  :math:`LRN(x)[obj][ch] = x[obj][ch] * / ((bias + alpha * sqrSum[obj][ch] / windowSize) ^ beta)`
  where :math:`obj` is index of the object , :math:`ch` is index of the channel,
  :math:`window_size`, :math:`bias`, :math:`alpha` and :math:`beta` are layer settings
  and :math:`sqrSum[obj][ch] = \sum_{i=\max(0, ch - \lfloor\frac{windowSize - 1}{2}\rfloor)}^{\min(C - 1, ch + \lceil\frac{windowSize - 1}{2}\rceil)}x[obj][i]^2`
  
  :param input_layer: The input layer and the number of its output. If no number
      is specified, the first output will be connected.
  :type input_layer: object, tuple(object, int)
  :param window_size: The size of window used in normalization
  :type: int, default=1
  :param bias: value added to the scaled sum of squares 
  :type: float, default=1.
  :param alpha: scale value of sum of squares
  :type: float, default=1e-4
  :param beta: exponent of the formula
  :type: float, default=0.75
  
  .. rubric:: Layer inputs:
  
  (1) the set of objects, of dimensions:
  
      - **BatchLength** * **BatchWidth** * **ListSize** * **Height** * **Width** * **Depth** - the number of objects
      - **Channels** - the size of the object
  
  .. rubric:: Layer outputs:
  
  (1) the result of the layer, of the dimensions of the input.
docstr: Sets the window size.
          
docstr_tok: ["Sets","the","window","size","."]
code: def window_size(self, window_size):
      """Sets the window size."""
      self._internal.set_window_size(int(window_size))
  
file_path: neoml/NeoML/Python/neoml/Dnn/Lrn.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 Lrn obj's underlying member method code with example usage: obj.window_size(window_size) without return types