class_docstr: Interface to concatenate data and transfer them to GPU asynchronously.
  
  It enables to transfer next batch of input data to GPU while GPU is
  running kernels for training using current batch of input data.
  
  An instance of this class is mainly intended to be used as a converter
  function of an updater like below.
  
  .. doctest::
  
      from chainer.dataset import convert
      ...
      updater = chainer.training.updaters.StandardUpdater(
                     ...,
                     converter=convert.ConcatWithAsyncTransfer(),
                     ...)
  
  Args:
      stream (cupy.cuda.Stream): CUDA stream. If ``None``, a stream is
          automatically created on the first call. Data transfer operation
          is launched asynchronously using the stream.
      compute_stream(cupy.cuda.Stream): CUDA stream used for compute kernels.
          If not ``None``, CUDA events are created/used to avoid global
          synchronization and overlap execution of compute kernels and data
          transfers as much as possible. If ``None``, global synchronization
          is used instead.
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code: def _get_conveyor(self):
      return Conveyor(self._device, self._stream)
  
imports: import collections
  import numpy
  import six
  import chainer
  from chainer import backend
  from chainer.backends import cuda
label_desc: Use image node_id 2 for calling the ConcatWithAsyncTransfer obj's underlying member method code with example usage: obj._get_conveyor() and returns: Conveyor