def METHOD_NAME(param, **kwargs):
    r"""
    Initialize the input tensor with Kaiming Normal initialization.
    This function implements the `param` initialization from the paper
    `Delving Deep into Rectifiers: Surpassing Human-Level Performance on
    ImageNet Classification <https://arxiv.org/abs/1502.01852>`
    by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. This is a
    robust initialization method that particularly considers the rectifier
    nonlinearities. In case of Uniform distribution, the range is [-x, x], where
    .. math::
        x = \sqrt{\\frac{6.0}{fan\_in}}
    In case of Normal distribution, the mean is 0 and the standard deviation
    is
    .. math::
        \sqrt{\\frac{2.0}{fan\_in}}
    Args:
        param (Tensor): Tensor that needs to be initialized.
    Examples:
        from paddleseg.cvlibs import param_init
        import paddle.nn as nn
        linear = nn.Linear(2, 4)
        # uniform is used to decide whether to use uniform or normal distribution
        param_init.kaiming_normal_init(linear.weight)
    """
    initializer = nn.initializer.KaimingNormal(**kwargs)
    initializer(param, param.block)