code: def he_normal(t):
      """He normal initializer.
  
      It draws samples from a truncated normal distribution centered on 0
      with `stddev = sqrt(2 / fan_in)`
      where `fan_in` is the number of input units in the weight tensor.
  
      # Arguments
          t(Tensor):the tensor to be filled in.
  
      # References
          - [Delving Deep into Rectifiers: Surpassing Human-Level Performance on
             ImageNet Classification](http://arxiv.org/abs/1502.01852)
      """
      _random_fill(t, scale=2.0, mode="fan_in", distribution="normal")
  
file_path: singa/python/singa/initializer.py
imports: from __future__ import division
  import math
  import numpy
  from deprecated import deprecated
label_desc: Use image node_id 6 for calling a global function with example usage: he_normal(t) without return types