code: def concat_examples(batch, device=None, padding=None):
      """Concat examples in minibatch.
  
      :param np.ndarray batch: The batch to concatenate
      :param int device: The device to send to
      :param Tuple[int,int] padding: The padding to use
      :return: (inputs, targets)
      :rtype (torch.Tensor, torch.Tensor)
      """
      x, t = convert.concat_examples(batch, padding=padding)
      x = torch.from_numpy(x)
      t = torch.from_numpy(t)
      if device is not None and device >= 0:
          x = x.cuda(device)
          t = t.cuda(device)
      return x, t
  
imports: import copy
  import json
  import logging
  import numpy
  import torch
  import torch.nn
  from chainer import Chain, reporter, training
  from chainer.dataset import convert
  from chainer.training import extensions
  from torch.nn.parallel import data_parallel
  from espnet.asr.asr_utils import snapshot_object, torch_load, torch_resume, torch_snapshot
  from espnet.lm.lm_utils import MakeSymlinkToBestModel, ParallelSentenceIterator, count_tokens, load_dataset, read_tokens
  from espnet.nets.lm_interface import LMInterface, dynamic_import_lm
  from espnet.optimizer.factory import dynamic_import_optimizer
  from espnet.scheduler.pytorch import PyTorchScheduler
  from espnet.scheduler.scheduler import dynamic_import_scheduler
  from espnet.utils.deterministic_utils import set_deterministic_pytorch
  from espnet.utils.training.evaluator import BaseEvaluator
  from espnet.utils.training.iterators import ShufflingEnabler
  from espnet.utils.training.tensorboard_logger import TensorboardLogger
  from espnet.utils.training.train_utils import check_early_stop, set_early_stop
label_desc: Use image node_id 2 for calling a global function with example usage: concat_examples(batch, device, padding) and returns: x,  t