code: def _concat_arrays(arrays, padding):
      # Convert `arrays` to numpy.ndarray if `arrays` consists of the built-in
      # types such as int, float or list.
      if not isinstance(arrays[0], chainer.get_array_types()):
          arrays = numpy.asarray(arrays)
  
      if padding is not None:
          arr_concat = _concat_arrays_with_padding(arrays, padding)
      else:
          device = backend.get_device_from_array(arrays[0])
          with chainer.using_device(device):
              arr_concat = device.xp.concatenate(
                  [array[None] for array in arrays]
              )
  
      return arr_concat
  
imports: import collections
  import numpy
  import six
  import chainer
  from chainer import backend
  from chainer.backends import cuda
label_desc: Use image node_id 6 for calling a global function with example usage: _concat_arrays(arrays, padding) and returns: arr_concat