class_docstr: Initializer that generates tensors with a uniform distribution.
  
  Parameters
  ----------
  minval : A python scalar or a scalar tensor.
      Lower bound of the range of random values to generate.
  maxval : A python scalar or a scalar tensor.
      Upper bound of the range of random values to generate.
  seed : A Python integer.
      Used to seed the random generator.
class_docstr_tok: ["Initializer","that","generates","tensors","with","a","uniform","distribution",".","Parameters","--","--","--","--","--","minval",":","A","python","scalar","or","a","scalar","tensor",".","Lower","bound","of","the","range","of","random","values","to","generate",".","maxval",":","A","python","scalar","or","a","scalar","tensor",".","Upper","bound","of","the","range","of","random","values","to","generate",".","seed",":","A","Python","integer",".","Used","to","seed","the","random","generator","."]
code: def get_config(self):
      return {
          "minval": self.minval,
          "maxval": self.maxval,
          "seed": self.seed,
      }
  
imports: import numpy
  import tensorflow
label_desc: Use image node_id 3 for calling the RandomUniform obj's underlying member method code with example usage: obj.get_config() and returns: dict