code: def lecun_normal(t):
      """LeCun normal initializer.
  
      It draws samples from a truncated normal distribution centered on 0
      with `stddev = sqrt(1 / 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
          - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
          - [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
      """
      _random_fill(t, scale=1.0, 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 7 for calling a global function with example usage: lecun_normal(t) without return types