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:
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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:
      """
      self.estimator.set_constant_gradient_clipping(min=min, max=max)
  
imports: from zoo.common.utils import enable_multi_fs_load, enable_multi_fs_save
  from zoo.orca.data.utils import row_to_sample, xshard_to_sample
  from zoo.orca.learn.utils import convert_predict_rdd_to_dataframe, bigdl_metric_results_to_dict, process_xshards_of_pandas_dataframe
  from zoo.pipeline.estimator.estimator import Estimator
  from zoo.orca.learn.ray_estimator import Estimator
  from zoo.orca.learn.pytorch.training_operator import TrainingOperator
  from zoo.orca.learn.spark_estimator import Estimator
  from zoo.orca.learn.optimizers import Optimizer, SGD
  from zoo.orca.learn.metrics import Accuracy
  from zoo.orca.data import SparkXShards
  from bigdl.optim.optimizer import MaxEpoch, OptimMethod
  from zoo.feature.common import FeatureSet
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from pyspark.sql import DataFrame
  import warnings
  import torch
  import types
label_desc: Use image node_id 16 for calling the PyTorchSparkEstimator obj's underlying member method code with example usage: obj.set_constant_gradient_clipping(min, max) without return types