normalize/norm函数

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def normalize(input, p=2, dim=1, eps=1e-12, out=None):
    # type: (Tensor, float, int, float, Optional[Tensor]) -> Tensor
    r"""Performs :math:`L_p` normalization of inputs over specified dimension.

    For a tensor :attr:`input` of sizes :math:`(n_0, ..., n_{dim}, ..., n_k)`, each
    :math:`n_{dim}` -element vector :math:`v` along dimension :attr:`dim` is transformed as

    .. math::
        v = \frac{v}{\max(\lVert v \rVert_p, \epsilon)}.

    With the default arguments it uses the Euclidean norm over vectors along dimension :math:`1` for normalization.

    Args:
        input: input tensor of any shape
        p (float): the exponent value in the norm formulation. Default: 2
        dim (int): the dimension to reduce. Default: 1
        eps (float): small value to avoid division by zero. Default: 1e-12
        out (Tensor, optional): the output tensor. If :attr:`out` is used, this
                                operation won't be differentiable.
    """
    if out is None:
        denom = input.norm(p, dim, True).clamp_min(eps).expand_as(input)#按维度求范数，0列，1行，默认为1，一般代表通道那个维度，然后指定最小值为eps，_代表原位操作，然后拓展成inputs的形状
        return input / denom
    else:
        denom = input.norm(p, dim, True).clamp_min(eps).expand_as(input)
        return torch.div(input, denom, out=out)