(input, mean, std, gamma, beta)
return _batchnormtrain.apply(input, mean, std, gamma, beta)
r"""Applies Batch Normalization over a 3d input that is seen as a
    mini-batch.

    .. _encoding.batchnormtrain:

    .. math::

        y = \frac{x - \mu[x]}{ \sqrt{var[x] + \epsilon}} * \gamma + \beta

    Shape:
        - Input: :math:`(N, C)` or :math:`(N, C, L)`
        - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
r"""Applies Batch Normalization over a 3d input that is seen as a
    mini-batch.
r Applies Batch Normalization over a 3d input that is seen as a mini - batch .
def batchnormtrain(input, mean, std, gamma, beta):
    r"""Applies Batch Normalization over a 3d input that is seen as a
    mini-batch.

    .. _encoding.batchnormtrain:

    .. math::

        y = \frac{x - \mu[x]}{ \sqrt{var[x] + \epsilon}} * \gamma + \beta

    Shape:
        - Input: :math:`(N, C)` or :math:`(N, C, L)`
        - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)

    """
    return _batchnormtrain.apply(input, mean, std, gamma, beta)
def batchnormtrain ( input , mean , std , gamma , beta ) : return _batchnormtrain . apply ( input , mean , std , gamma , beta )