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.
code: def __call__(self, shape: Sequence[int], dtype: Any) -> jax.Array:
      input_size = np.prod(shape[:-1])
      max_val = np.sqrt(3 / input_size) * self.scale
      return RandomUniform(-max_val, max_val)(shape, dtype)
  
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 2 for calling the UniformScaling obj's underlying member method code with example usage: obj.__call__(shape, dtype) and returns: RandomUniform