code: def move_optimizer_to_cuda(optimizer):
      """
      Move the optimizer state to GPU, if necessary.
      After calling, any parameter specific state in the optimizer
      will be located on the same device as the parameter.
      """
      for param_group in optimizer.param_groups:
          for param in param_group["params"]:
              if param.is_cuda:
                  param_state = optimizer.state[param]
                  for k in list(param_state.keys()):
                      if isinstance(param_state[k], torch.Tensor):
                          param_state[k] = param_state[k].cuda(
                              device=param.get_device()
                          )
  
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 3 for calling a global function with example usage: move_optimizer_to_cuda(optimizer) without return types