class_docstr: Initializes a blob using the Xavier algorithm: with random values
  from a normal distribution over (0, 1 / input_size).
  
  :param random_generator: Sets the random numbers generator to be used.
      By default, the standard NeoML randomizer with default seed is used.
  :type random_generator: object, default=None     
class_docstr_tok: ["Initializes","a","blob","using","the","Xavier","algorithm",":","with","random","values","from","a","normal","distribution","over","(","0",",","1","\/","input_size",")",".",":","param","random_generator",":","Sets","the","random","numbers","generator","to","be","used",".","By","default",",","the","standard","NeoML","randomizer","with","default","seed","is","used",".",":","type","random_generator",":","object",",","default=None"]
code: def __init__(self, random_generator=None):
      if isinstance(random_generator, PythonWrapper.Xavier):
          super().__init__(random_generator)
          return
  
      if random_generator is None:
          random_generator = neoml.Random.Random()
  
      if not isinstance(random_generator, neoml.Random.Random):
          raise ValueError(
              "The `random_generator` must be a neoml.Random.Random."
          )
  
      internal = PythonWrapper.Xavier(random_generator)
      super().__init__(internal)
  
file_path: neoml/NeoML/Python/neoml/Dnn/Initializer.py
imports: import neoml
  import neoml.PythonWrapper
  import neoml.Random
label_desc: Use image node_id 1 to create a new Xavier object from inherited base classes: Initializer with example: obj = Xavier(random_generator)