class_docstr: Initializes weights with random values uniformly sampled from a given range.
  
  Parameters
  ----------
  scale : float, optional
      The bound on the range of the generated random values.
      Values are generated from the range [0, `scale`].
      Default scale is 1.
  
  Example
  -------
  >>> # Given 'module', an instance of 'mxnet.module.Module', initialize weights
  >>> # to random values uniformly sampled between 0 and 0.1.
  ...
  >>> init = ZeroUniform(0.1)
  >>> module.init_params(init)
  >>> for dictionary in module.get_params():
  ...     for key in dictionary:
  ...         print(key)
  ...         print(dictionary[key].asnumpy())
  ...
  fullyconnected0_weight
  [[ 0.01360891 0.02144304  0.08511933]]
code: def __init__(self, scale=1):
      super(ZeroUniform, self).__init__(scale=scale)
      self.scale = scale
  
file_path: gluon-cv/gluoncv/model_zoo/alpha_pose/utils.py
imports: from mxnet import random
  from mxnet import ndarray
  from mxnet.initializer import Initializer
label_desc: Use image node_id 1 to create a new ZeroUniform object from inherited base classes: Initializer with example: obj = ZeroUniform(scale)