parameters: (seed=None)
return_statement: return VarianceScaling(
        scale=2., mode="fan_in", distribution="truncated_normal", seed=seed)
docstring: He normal initializer.
  
    It draws samples from a truncated normal distribution centered on 0
    with standard deviation (after truncation) given by
    `stddev = sqrt(2 / 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:
        [He et al., 2015]
        (https://www.cv-foundation.org/openaccess/content_iccv_2015/html/He_Delving_Deep_into_ICCV_2015_paper.html)
        # pylint: disable=line-too-long
        ([pdf](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf))
docstring_summary: He normal initializer.
docstring_tokens: He normal initializer .
function: def he_normal(seed=None):
    """He normal initializer.
  
    It draws samples from a truncated normal distribution centered on 0
    with standard deviation (after truncation) given by
    `stddev = sqrt(2 / 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:
        [He et al., 2015]
        (https://www.cv-foundation.org/openaccess/content_iccv_2015/html/He_Delving_Deep_into_ICCV_2015_paper.html)
        # pylint: disable=line-too-long
        ([pdf](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf))
    """
    return VarianceScaling(
        scale=2., mode="fan_in", distribution="truncated_normal", seed=seed)
function_tokens: def he_normal ( seed = None ) : return VarianceScaling ( scale = 2. , mode = "fan_in" , distribution = "truncated_normal" , seed = seed )