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: Clear gradient clipping parameters. In this case, gradient clipping will not be applied.
  In order to take effect, it needs to be called before fit.
  :return:
docstr_tok: ["Clear","gradient","clipping","parameters",".","In","this","case",",","gradient","clipping","will","not","be","applied",".","In","order","to","take","effect",",","it","needs","to","be","called","before","fit",".",":","return",":"]
code: def clear_gradient_clipping(self):
      """
      Clear gradient clipping parameters. In this case, gradient clipping will not be applied.
      In order to take effect, it needs to be called before fit.
      :return:
      """
      callZooFunc(self.bigdl_type, "clearGradientClipping", self.value)
  
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 2 for calling the Estimator obj's underlying member method code with example usage: obj.clear_gradient_clipping() without return types