class_docstr: Initializes a blob using uniform distribution between the set bounds.
  
  :param lower_bound: The lower bound of the distribution.
  :type lower_bound: float, default=-1.0
  :param upper_bound: The upper bound of the distribution.
  :type upper_bound: float, default=1.0
  :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","uniform","distribution","between","the","set","bounds",".",":","param","lower_bound",":","The","lower","bound","of","the","distribution",".",":","type","lower_bound",":","float",",","default=-1.0",":","param","upper_bound",":","The","upper","bound","of","the","distribution",".",":","type","upper_bound",":","float",",","default=1.0",":","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, lower_bound=-1.0, upper_bound=1.0, random_generator=None
  ):
      if isinstance(lower_bound, PythonWrapper.Uniform):
          super().__init__(lower_bound)
          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.Uniform(
          float(lower_bound), float(upper_bound), 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 Uniform object from inherited base classes: Initializer with example: obj = Uniform(lower_bound, upper_bound, random_generator)