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 get_config(self):
      return {
          "mean": self.mean,
          "stddev": self.stddev,
          "seed": self.seed,
      }
  
imports: import numpy
  import tensorflow
label_desc: Use image node_id 3 for calling the RandomNormal obj's underlying member method code with example usage: obj.get_config() and returns: dict