code: def converter():
      """Decorator to make a converter.
  
      This decorator turns a converter function into a
      :class:`chainer.dataset.Converter` class instance, which also is a
      callable.
      This is required to use the converter function from an old module that
      does not support :class:`chainer.backend.Device` instances
      (See the **Device argument conversion** section below).
  
      .. rubric:: Requirements of the target function
  
      The target converter function must accept two positional arguments:
      a batch and a device, and return a converted batch.
  
      The type of the device argument is :class:`chainer.backend.Device`.
  
      The types and values of the batches (the first argument and the return
      value) are not specified: they depend on how the converter is used (e.g.
      by updaters).
  
      .. admonition:: Example
  
          >>> @chainer.dataset.converter()
          ... def custom_converter(batch, device):
          ...     assert isinstance(device, chainer.backend.Device)
          ...     # do something with batch...
          ...     return device.send(batch)
  
      .. rubric:: Device argument conversion
  
      For backward compatibility, the decorator wraps
      the function so that if the converter is called with the device argument
      with ``int`` type, it is converted to a :class:`chainer.backend.Device`
      instance before calling the original function. The ``int`` value indicates
      the CUDA device of the cupy backend.
  
      Without the decorator, the converter cannot support ChainerX devices.
      If the batch were requested to be converted to ChainerX with such
      converters, :class:`RuntimeError` will be raised.
  
      .. note::
  
          Converters using this decorator can't be pickled
          causing :class:`chainer.training.updaters.MultiprocessParallelUpdater`
          to fail when the multiprocessing start mode is set to ``'spawn'`` or
          ``'forkserver'``. Should you need to use such feature, please rely on
          class style converters.
  
      """
  
      def wrap(func):
          return _ArbitraryCallableConverter(func)
  
      return wrap
  
imports: import collections
  import numpy
  import six
  import chainer
  from chainer import backend
  from chainer.backends import cuda
label_desc: Use image node_id 1 for calling a global function with example usage: converter() and returns: wrap, _ArbitraryCallableConverter