code: def move_model_to_cuda(self, args):
      if not torch.cuda.is_available():
          logger.error("No CUDA found!")
          exit(-1)
      logger.info("Moving to CUDA...")
      self._model_device = "cuda"
      self.bert_model.cuda()
      logger.info("# GPUs = %d" % (torch.cuda.device_count()))
      if (
          torch.cuda.device_count() > 1
          and args.entity_single_card == False
      ):
          self.bert_model = torch.nn.DataParallel(self.bert_model)
  
imports: import os
  import sys
  import torch
  from torch import nn
  import torch.nn.functional
  from torch.nn import CrossEntropyLoss
  from allennlp.nn.util import batched_index_select
  from allennlp.nn import util
  from allennlp.modules import FeedForward
  import numpy
  from transformers import BertTokenizer, BertPreTrainedModel, BertModel
  from transformers import AlbertTokenizer, AlbertPreTrainedModel, AlbertModel
  import os
  import json
  import logging
label_desc: Use image node_id 2 for calling the EntityModel obj's underlying member method code with example usage: obj.move_model_to_cuda(args) without return types