class_docstr: The layer that converts a bitset into vectors of ones and zeros.
  
  :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 bit_set_size: The size of the bitset. 
  :type bit_set_size: int, > 0
  :param name: The layer name.
  :type name: str, default=None
  
  .. rubric:: Layer inputs:
  
  (1) a blob with int data containing bitsets. 
      The dimensions:
  
      - **BatchLength** * **BatchWidth** * **ListSize** * **Height** * **Width** * **Depth**
        is the number of bitsets
      - **Channels** is bitset itself
  
  .. rubric:: Layer outputs:
  
  (1) a blob with the result of vectorization.
      The dimensions:
  
      - **Channels** is equal to bit_set_size
      - the other dimensions are the same as for the input
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docstr: Sets the bitset size.
          
docstr_tok: ["Sets","the","bitset","size","."]
code: def bit_set_size(self, bit_set_size):
      """Sets the bitset size."""
      self._internal.set_bit_set_size(bit_set_size)
  
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 3 for calling the BitSetVectorization obj's underlying member method code with example usage: obj.bit_set_size(bit_set_size) without return types