code: def glorot_normal(t):
      """Glorot normal initializer, also called Xavier normal initializer.
  
      It draws samples from a normal distribution centered on 0
      with `stddev = sqrt(2 / (fan_in + fan_out))`
      where `fan_in` is the number of input units in the weight tensor
      and `fan_out` is the number of output units in the weight tensor.
  
      # Arguments
          t(Tensor):the tensor to be filled in.
  
      # References
          - [Understanding the difficulty of training deep feedforward neural
             networks](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf)
      """
      _random_fill(t, scale=1.0, mode="fan_avg", 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 4 for calling a global function with example usage: glorot_normal(t) without return types