parameters: (seed=None)
return_statement: return VarianceScaling(
        scale=1., mode="fan_in", distribution="uniform", seed=seed)
docstring: LeCun uniform initializer.
  
    It draws samples from a uniform distribution within [-limit, limit]
    where `limit` is `sqrt(3 / 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)
        # pylint: disable=line-too-long
        ([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 uniform initializer.
docstring_tokens: LeCun uniform initializer .
function: def lecun_uniform(seed=None):
    """LeCun uniform initializer.
  
    It draws samples from a uniform distribution within [-limit, limit]
    where `limit` is `sqrt(3 / 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)
        # pylint: disable=line-too-long
        ([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="uniform", seed=seed)
function_tokens: def lecun_uniform ( seed = None ) : return VarianceScaling ( scale = 1. , mode = "fan_in" , distribution = "uniform" , seed = seed )