class_docstr: Estimator class for training and evaluation BigDL models.
  
  Estimator wraps a model, and provide an uniform training, evaluation or prediction operation on
  both local host and distributed spark environment.
class_docstr_tok: ["Estimator","class","for","training","and","evaluation","BigDL","models",".","Estimator","wraps","a","model",",","and","provide","an","uniform","training",",","evaluation","or","prediction","operation","on","both","local","host","and","distributed","spark","environment","."]
docstr: Set constant gradient clipping during the training process.
  In order to take effect, it needs to be called before fit.
  :param min: The minimum value to clip by.
  :param max: The maximum value to clip by.
  :return:
docstr_tok: ["Set","constant","gradient","clipping","during","the","training","process",".","In","order","to","take","effect",",","it","needs","to","be","called","before","fit",".",":","param","min",":","The","minimum","value","to","clip","by",".",":","param","max",":","The","maximum","value","to","clip","by",".",":","return",":"]
code: def set_constant_gradient_clipping(self, min, max):
      """
      Set constant gradient clipping during the training process.
      In order to take effect, it needs to be called before fit.
      :param min: The minimum value to clip by.
      :param max: The maximum value to clip by.
      :return:
      """
      callZooFunc(
          self.bigdl_type,
          "setConstantGradientClipping",
          self.value,
          min,
          max,
      )
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/estimator/estimator.py
imports: from bigdl.util.common import JavaValue
  from zoo.common.utils import callZooFunc
label_desc: Use image node_id 3 for calling the Estimator obj's underlying member method code with example usage: obj.set_constant_gradient_clipping(min, max) without return types