class_docstr: Initializes array with a scaled uniform distribution.
  
  Each element of the array is initialized by the value drawn
  independently from uniform distribution :math:`[-scale, scale]`.
  
  Attributes:
      scale (float): A constant that determines the
          scale of the uniform distribution.
      dtype: Data type specifier.
      rng (xp.random.RandomState): Pseudo-random number generator.
class_docstr_tok: ["Initializes","array","with","a","scaled","uniform","distribution",".","Each","element","of","the","array","is","initialized","by","the","value","drawn","independently","from","uniform","distribution",":","math",":","`","[","-scale",",","scale","]","`",".","Attributes",":","scale","(","float",")",":","A","constant","that","determines","the","scale","of","the","uniform","distribution",".","dtype",":","Data","type","specifier",".","rng","(","xp.random.RandomState",")",":","Pseudo-random","number","generator","."]
code: def __init__(self, scale=0.05, dtype=None, **kwargs):
      self.scale = scale
      rng = None
      if kwargs:
          (rng,) = argument.parse_kwargs(kwargs, ("rng", rng))
      self.rng = rng
      super(Uniform, self).__init__(dtype)
  
imports: import numpy
  from chainer import backend
  from chainer import initializer
  from chainer.utils import argument
label_desc: Use image node_id 1 to create a new Uniform object from inherited base classes: initializer with example: obj = Uniform(scale, dtype)