code: def _enable_gradient_clipping(self):
      if self._grad_clipping is not None:
          # Pylint is unable to tell that we're in the case that _grad_clipping is not None...
          # pylint: disable=invalid-unary-operand-type
          clip_function = lambda grad: grad.clamp(
              -self._grad_clipping, self._grad_clipping
          )
          for parameter in self._model.parameters():
              if parameter.requires_grad:
                  parameter.register_hook(clip_function)
  
imports: from __future__ import with_statement
  from __future__ import division
  from __future__ import absolute_import
  import logging
  import os
  import shutil
  import time
  import re
  import datetime
  import traceback
  import torch
  import torch.optim.lr_scheduler
  from torch.nn.parallel import replicate, parallel_apply
  from torch.nn.parallel.scatter_gather import scatter_kwargs, gather
  from allennlp.common import Params
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.util import peak_memory_mb, gpu_memory_mb
  from allennlp.common.tqdm import Tqdm
  from allennlp.data.instance import Instance
  from allennlp.data.iterators.data_iterator import DataIterator
  from allennlp.models.model import Model
  from allennlp.nn import util
  from allennlp.training.learning_rate_schedulers import LearningRateScheduler
  from allennlp.training.optimizers import Optimizer
label_desc: Use image node_id 2 for calling the Trainer obj's underlying member method code with example usage: obj._enable_gradient_clipping() without return types