class_docstr: This tuner is essentially grid search, but it guarantees all the parameters with alpha * beta^2 * gamma^2 is
  approximately `product`.
class_docstr_tok: ["This","tuner","is","essentially","grid","search",",","but","it","guarantees","all","the","parameters","with","alpha","*","beta^2","*","gamma^2","is","approximately","`","product","`","."]
docstr: :param product: the constant provided, should be 2 in EfficientNet-B1
docstr_tok: [":","param","product",":","the","constant","provided",",","should","be","2","in","EfficientNet-B1"]
code: def __init__(self, product):
      """
      :param product: the constant provided, should be 2 in EfficientNet-B1
      """
      super().__init__()
      self.product = product
  
imports: from nni.algorithms.hpo.gridsearch_tuner import GridSearchTuner
label_desc: Use image node_id 1 to create a new FixedProductTuner object from inherited base classes: GridSearchTuner with example: obj = FixedProductTuner(product)