class_docstr: The layer that performs object normalization using the formula:
  :math:`objectNorm(x)[i][j] = ((x[i][j] - mean[i]) / \sqrt{var[i] + epsilon}) * scale[j] + bias[j]`
  
  :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 epsilon: The small value added to the variance to avoid division by zero. 
  :type epsilon: float, default=0.00001
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) a set of objects.
      The dimensions:
  
      - **BatchLength** * **BatchWidth** * **ListSize** is the number of objects
      - **Height** * **Width** * **Depth** * **Channels** is the object size
  
  .. rubric:: Layer outputs:
  
  (1) the normalized result, of the same size as the input.
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docstr: Gets the small value used to avoid division by zero.
          
docstr_tok: ["Gets","the","small","value","used","to","avoid","division","by","zero","."]
code: def epsilon(self):
      """Gets the small value used to avoid division by zero."""
      return self._internal.get_epsilon()
  
file_path: neoml/NeoML/Python/neoml/Dnn/ObjectNormalization.py
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
  import neoml.Blob
label_desc: Use image node_id 2 for calling the ObjectNormalization obj's underlying member method code with example usage: obj.epsilon() and returns: self