class_docstr: Initializes parameter tensors with values drawn from a uniform distribution
  ranging from :math:`-K` to :math:`K`. We define :math:`K=\sqrt{6 / (n_{in} + n_{out})}`,
  where :math:`n_{in}` and :math:`n_{out}` are the input and output dimensions, respectively,
  of the parameter tensor. This approach normalizes the range of the initialized values
  by the tensor dimensions.
  
  From: "Understanding the difficulty of training deep feedforward neural networks"
  (http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf).
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docstr: Fill the provided tensor with random values drawn from the Uniform
  distribution, using normalized bounds.
  
  Args:
      params (tensor): Tensor to fill
docstr_tok: ["Fill","the","provided","tensor","with","random","values","drawn","from","the","Uniform","distribution",",","using","normalized","bounds",".","Args",":","params","(","tensor",")",":","Tensor","to","fill"]
code: def fill(self, param):
      """
      Fill the provided tensor with random values drawn from the Uniform
      distribution, using normalized bounds.
  
      Args:
          params (tensor): Tensor to fill
      """
      k = np.sqrt(6.0 / (param.shape[0] + param.shape[1]))
      param[:] = self.be.rng.uniform(-k, k, param.shape)
  
imports: import numpy
  from neon import NervanaObject
  from neon.backends.backend import Tensor
label_desc: Use image node_id 2 for calling the GlorotUniform obj's underlying member method code with example usage: obj.fill(param) without return types