class_docstr: He uniform variance scaling initializer.
  
  It draws samples from a uniform distribution within [-limit, limit]
  where ``limit`` is ``sqrt(6 / fan_in)``
  where ``fan_in`` is the number of input units in the weight tensor.
  # Arguments
  seed: A Python integer. Used to seed the random generator.
  # Returns
  An initializer.
  # References
  He et al., https://arxiv.org/abs/1502.01852
class_docstr_tok: ["He","uniform","variance","scaling","initializer",".","It","draws","samples","from","a","uniform","distribution","within","[","-limit",",","limit","]","where","``","limit","``","is","``","sqrt","(","6","\/","fan_in",")","``","where","``","fan_in","``","is","the","number","of","input","units","in","the","weight","tensor",".","#","Arguments","seed",":","A","Python","integer",".","Used","to","seed","the","random","generator",".","#","Returns","An","initializer",".","#","References","He","et","al.",",","https",":","\/\/arxiv.org\/abs\/1502.01852"]
docstr: create an instance of the initializer
docstr_tok: ["create","an","instance","of","the","initializer"]
code: def get_instance(args):
      # pylint: disable=unused-argument
      """
      create an instance of the initializer
      """
      if not args:
          args = {
              "scale": "2.",
              "mode": "fan_in",
              "distribution": "uniform",
          }
      return VarianceScaling.get_instance(args)
  
imports: import tensorflow
label_desc: Use image node_id 1 for calling the HeUniform obj's underlying member method code with example usage: obj.get_instance(args) and returns: VarianceScaling