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 __call__(self, array):
      if self.dtype is not None:
          assert array.dtype == self.dtype, "{} != {}".format(
              array.dtype, self.dtype
          )
      if self.rng is None:
          device = backend.get_device_from_array(array)
          array[...] = device.xp.random.uniform(
              low=-self.scale, high=self.scale, size=array.shape
          )
      else:
          backend.copyto(
              array,
              self.rng.uniform(
                  low=-self.scale, high=self.scale, size=array.shape
              ).astype(array.dtype, copy=False),
          )
  
imports: import numpy
  from chainer import backend
  from chainer import initializer
  from chainer.utils import argument
label_desc: Use image node_id 2 for calling the Uniform obj's underlying member method code with example usage: obj.__call__(array) without return types