class_docstr: Initializer that generates tensors with a normal distribution.
  
  Parameters
  ----------
  mean : A python scalar or a scalar tensor.
      Mean of the random values to generate.
  stddev : A python scalar or a scalar tensor.
      Standard deviation of the random values to generate.
  seed : A Python integer.
      Used to seed the random generator.
class_docstr_tok: ["Initializer","that","generates","tensors","with","a","normal","distribution",".","Parameters","--","--","--","--","--","mean",":","A","python","scalar","or","a","scalar","tensor",".","Mean","of","the","random","values","to","generate",".","stddev",":","A","python","scalar","or","a","scalar","tensor",".","Standard","deviation","of","the","random","values","to","generate",".","seed",":","A","Python","integer",".","Used","to","seed","the","random","generator","."]
code: def __init__(self, mean=0.0, stddev=0.05, seed=None):
      self.mean = mean
      self.stddev = stddev
      self.seed = seed
  
imports: import numpy
  import tensorflow
label_desc: Use image node_id 1 to create a new RandomNormal object from inherited base classes: Initializer with example: obj = RandomNormal(mean, stddev, seed)