class_docstr: Initializes parameters with a zero-mean Gaussian distribution. The standard deviation
  is automatically set as :math:`\sigma=\sqrt{2 / n_{in}}`, where :math:`n_{in}` is
  the input dimension of the tensor.
  
  
  Based on the initializer described in: http://arxiv.org/pdf/1502.01852.pdf.
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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="Kaiming"):
      """
      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(Kaiming, 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 Kaiming object from inherited base classes: Initializer with example: obj = Kaiming(local, name)