class_docstr: Uniform scaling initializer.
  
  Initializes by sampling from a uniform distribution, but with the variance
  scaled by the inverse square root of the number of input units, multiplied by
  the scale.
docstr: Constructs the :class:`UniformScaling` initializer.
  
  Args:
    scale: Scale to multiply the upper limit of the uniform distribution by.
docstr_tok: ["Constructs","the",":","class",":","`","UniformScaling","`","initializer",".","Args",":","scale",":","Scale","to","multiply","the","upper","limit","of","the","uniform","distribution","by","."]
code: def __init__(self, scale=1.0):
      """Constructs the :class:`UniformScaling` initializer.
  
      Args:
        scale: Scale to multiply the upper limit of the uniform distribution by.
      """
      self.scale = scale
  
imports: from collections.abc import Sequence
  from typing import Any, Union
  from haiku._src import base
  from haiku._src.typing import Initializer
  import jax
  import jax.numpy
  import numpy
label_desc: Use image node_id 1 to create a new UniformScaling object from inherited base classes: hk.initializers with example: obj = UniformScaling(scale)