class_docstr: Initializes parameter tensors with values drawn from a uniform distribution
  ranging from :math:`-K` to :math:`K` We define :math:`K=\sqrt{3 / (n_{in})}`,
  where :math:`n_{in}` is the number of input nodes.
  
  Similar to Glorot except the range is normalized by the input size only.
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docstr: Class constructor.
  
  Args:
      local (bool, optional): Whether the layer type is local (Convolutional) or not.
                                Default is True.
      name (string, optional): Name to assign an instance of this class.
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code: def __init__(self, local=True, name="xavier"):
      """
      Class constructor.
  
      Args:
          local (bool, optional): Whether the layer type is local (Convolutional) or not.
                                    Default is True.
          name (string, optional): Name to assign an instance of this class.
      """
      super(Xavier, self).__init__(name=name)
      self.local = local
  
imports: import numpy
  from neon import NervanaObject
  from neon.backends.backend import Tensor
label_desc: Use image node_id 1 to create a new Xavier object from inherited base classes: Initializer with example: obj = Xavier(local, name)