parameters: (seed=None)
return_statement: return VarianceScaling(
        scale=1., mode="fan_in", distribution="truncated_normal", seed=seed)
docstring: LeCun normal initializer.
  
    It draws samples from a truncated normal distribution centered on 0
    with `stddev = sqrt(1 / fan_in)`
    where `fan_in` is the number of input units in the weight tensor.
  
    Arguments:
      seed: A Python integer. Used to seed the random generator.
  
    Returns:
      An initializer.
  
    References:
        - Self-Normalizing Neural Networks,
        [Klambauer et al., 2017]
        (https://papers.nips.cc/paper/6698-self-normalizing-neural-networks)
        ([pdf]
        (https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf))
        - Efficient Backprop,
        [Lecun et al., 1998](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
docstring_summary: LeCun normal initializer.
docstring_tokens: LeCun normal initializer .
function: def lecun_normal(seed=None):
    """LeCun normal initializer.
  
    It draws samples from a truncated normal distribution centered on 0
    with `stddev = sqrt(1 / fan_in)`
    where `fan_in` is the number of input units in the weight tensor.
  
    Arguments:
      seed: A Python integer. Used to seed the random generator.
  
    Returns:
      An initializer.
  
    References:
        - Self-Normalizing Neural Networks,
        [Klambauer et al., 2017]
        (https://papers.nips.cc/paper/6698-self-normalizing-neural-networks)
        ([pdf]
        (https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf))
        - Efficient Backprop,
        [Lecun et al., 1998](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
    """
    return VarianceScaling(
        scale=1., mode="fan_in", distribution="truncated_normal", seed=seed)
function_tokens: def lecun_normal ( seed = None ) : return VarianceScaling ( scale = 1. , mode = "fan_in" , distribution = "truncated_normal" , seed = seed )