docstr: Performs gradient rescaling. Is a no-op if gradient rescaling is not enabled.
docstr_tok: ["Performs","gradient","rescaling",".","Is","a","no-op","if","gradient","rescaling","is","not","enabled","."]
code: def _rescale_gradients(self):
      """
      Performs gradient rescaling. Is a no-op if gradient rescaling is not enabled.
      """
      if self._grad_norm:
          parameters_to_clip = [
              p for p in self._model.parameters() if p.grad is not None
          ]
          return sparse_clip_norm(parameters_to_clip, self._grad_norm)
      return None
  
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 4 for calling the Trainer obj's underlying member method code with example usage: obj._rescale_gradients() and returns: None, sparse_clip_norm