code: def kaiming_normal_init(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)
  
imports: import paddle.nn
label_desc: Use image node_id 3 for calling a global function with example usage: kaiming_normal_init(param) without return types