class_docstr: The layer that converts enumeration values into one-hot encoding.
  
  :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 enum_size: The number of constants in the enumeration.
  :type enum_size: int, > 0
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) a blob with int or float data that contains enumeration values.
      The dimensions:
  
      - **Channels** is 1
      - the other dimensions may be of any length
  
  .. rubric:: Layer outputs:
  
  (1) a blob with the vectors that one-hot encode the enumeration values.
      The dimensions:
  
      - **Channels** is enum_size
      - the other dimensions stay the same as in the first input
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code: def __init__(self, input_layer, enum_size, name=None):
      if type(input_layer) is PythonWrapper.EnumBinarization:
          super().__init__(input_layer)
          return
  
      layers, outputs = check_input_layers(input_layer, 1)
  
      internal = PythonWrapper.EnumBinarization(
          str(name), layers[0], int(outputs[0]), enum_size
      )
      super().__init__(internal)
  
file_path: neoml/NeoML/Python/neoml/Dnn/Binarization.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 EnumBinarization object from inherited base classes: Layer with example: obj = EnumBinarization(input_layer, enum_size, name)