code: def all_to_cuda(data, device=None):
      if isinstance(data, torch.Tensor):
          data = to_cuda(data, device)
      elif isinstance(data, (list, dict)):
          keys = (
              range(len(data))
              if isinstance(data, list)
              else data.keys()
          )
          for k in keys:
              if isinstance(data[k], torch.Tensor):
                  data[k] = to_cuda(data[k], device)
  
      return data
  
imports: import argparse
  import multiprocessing
  import os
  import platform
  import time
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  import torch.nn
  import torch.optim
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.squad import SQuADDataset
  from graph4nlp.pytorch.models.graph2seq import Graph2Seq
  from graph4nlp.pytorch.models.graph2seq_loss import Graph2SeqLoss
  from graph4nlp.pytorch.modules.config import get_basic_args
  from graph4nlp.pytorch.modules.evaluation import BLEU, METEOR, ROUGE
  from graph4nlp.pytorch.modules.graph_embedding_initialization.embedding_construction import WordEmbedding
  from graph4nlp.pytorch.modules.utils import constants
  from graph4nlp.pytorch.modules.utils.config_utils import get_yaml_config, update_values
  from graph4nlp.pytorch.modules.utils.copy_utils import prepare_ext_vocab
  from graph4nlp.pytorch.modules.utils.generic_utils import EarlyStopping, grid, to_cuda
  from graph4nlp.pytorch.modules.utils.logger import Logger
  from .fused_embedding_construction import FusedEmbeddingConstruction
label_desc: Use image node_id 3 for calling a global function with example usage: all_to_cuda(data, device) and returns: data