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: Clip gradient to a maximum L2-Norm during the training process.
  In order to take effect, it needs to be called before fit.
  :param clip_norm: Gradient L2-Norm threshold.
  :return:
docstr_tok: ["Clip","gradient","to","a","maximum","L2-Norm","during","the","training","process",".","In","order","to","take","effect",",","it","needs","to","be","called","before","fit",".",":","param","clip_norm",":","Gradient","L2-Norm","threshold",".",":","return",":"]
code: def set_l2_norm_gradient_clipping(self, clip_norm):
      """
      Clip gradient to a maximum L2-Norm during the training process.
      In order to take effect, it needs to be called before fit.
      :param clip_norm: Gradient L2-Norm threshold.
      :return:
      """
      callZooFunc(
          self.bigdl_type,
          "setGradientClippingByL2Norm",
          self.value,
          clip_norm,
      )
  
file_path: BigDL/python/dllib/src/bigdl/dllib/estimator/estimator.py
imports: from bigdl.dllib.utils.common import JavaValue
  from bigdl.dllib.utils.file_utils import callZooFunc
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 4 for calling the Estimator obj's underlying member method code with example usage: obj.set_l2_norm_gradient_clipping(clip_norm) without return types