text: import numpy
  
  from chainer.backends import cuda
  from chainer.functions.connection import deconvolution_2d
  from chainer import initializers
  from chainer import link
  from chainer.utils import argument
  from chainer import variable
  
  
  class Deconvolution2D(link.Link):
  
      """__init__(self, in_channels, out_channels, ksize=None, stride=1, pad=0, \
  nobias=False, outsize=None, initialW=None, initial_bias=None, *, groups=1)
  
      Two dimensional deconvolution function.
  
      This link wraps the :func:`~chainer.functions.deconvolution_2d` function
      and holds the filter weight and bias vector as parameters.
  
      Deconvolution links can use a feature of cuDNN called autotuning, which
      selects the most efficient CNN algorithm for images of fixed-size,
      can provide a significant performance boost for fixed neural nets.
      To enable, set `chainer.using_config('autotune', True)`
  
      .. warning::
  
          ``deterministic`` argument is not supported anymore since v2.
          Instead, use ``chainer.using_config('cudnn_deterministic', value)``
          (value is either ``True`` or ``False``).
          See :func:`chainer.using_config`.
  
      Args:
          in_channels (int or None): Number of channels of input arrays.
              If ``None``, parameter initialization will be deferred until the
              first forward data pass at which time the size will be determined.
          out_channels (int): Number of channels of output arrays.
          ksize (int or pair of ints): Size of filters (a.k.a. kernels).
              ``ksize=k`` and ``ksize=(k, k)`` are equivalent.
          stride (int or pair of ints): Stride of filter applications.
              ``stride=s`` and ``stride=(s, s)`` are equivalent.
          pad (int or pair of ints): Spatial padding width for input arrays.
              ``pad=p`` and ``pad=(p, p)`` are equivalent.
          nobias (bool): If ``True``, then this function does not use the bias
              term.
          outsize (tuple): Expected output size of deconvolutional operation.
              It should be pair of height and width :math:`(out_H, out_W)`.
              Default value is ``None`` and the outsize is estimated by
              input size, stride and pad.
          initialW (:ref:`initializer <initializer>`): Initializer to
              initialize the weight. When it is :class:`numpy.ndarray`,
              its ``ndim`` should be 4.
          initial_bias (:ref:`initializer <initializer>`): Initializer to
              initialize the bias. If ``None``, the bias will be initialized to
              zero. When it is :class:`numpy.ndarray`, its ``ndim`` should be 1.
          groups (int): The number of groups to use grouped deconvolution. The
              default is one, where grouped deconvolution is not used.
  
      The filter weight has four dimensions :math:`(c_I, c_O, k_H, k_W)`
      which indicate the number of input channels, output channels,
      height and width of the kernels, respectively.
      The filter weight is initialized with i.i.d. Gaussian random samples, each
      of which has zero mean and deviation :math:`\\sqrt{1/(c_I k_H k_W)}` by
      default.
  
      The bias vector is of size :math:`c_O`.
      Its elements are initialized by ``bias`` argument.
      If ``nobias`` argument is set to True, then this function does not hold
      the bias parameter.
  
      The output of this function can be non-deterministic when it uses cuDNN.
      If ``chainer.configuration.config.cudnn_deterministic`` is ``True`` and
      cuDNN version is >= v3, it forces cuDNN to use a deterministic algorithm.
  
      .. seealso::
         See :func:`chainer.functions.deconvolution_2d` for the definition of
         two-dimensional convolution.
  
      .. seealso::
          See :func:`chainer.links.Convolution2D` for the examples of ways to
          give arguments to this link.
  
      .. admonition:: Example
  
          There are several ways to make a Deconvolution2D link.
  
          Let an input vector ``x`` be:
  
          >>> x = np.arange(1 * 3 * 10 * 10, dtype=np.float32).reshape(
          ...     1, 3, 10, 10)
  
          1. Give the first three arguments explicitly:
  
              In this case, all the other arguments are set to the default
              values.
  
              >>> l = L.Deconvolution2D(3, 7, 4)
              >>> y = l(x)
              >>> y.shape
              (1, 7, 13, 13)
  
          2. Omit ``in_channels`` or fill it with ``None``:
  
              The below two cases are the same.
  
              >>> l = L.Deconvolution2D(7, 4)
              >>> y = l(x)
              >>> y.shape
              (1, 7, 13, 13)
  
              >>> l = L.Deconvolution2D(None, 7, 4)
              >>> y = l(x)
              >>> y.shape
              (1, 7, 13, 13)
  
              When you omit the first argument, you need to specify the other
              subsequent arguments from ``stride`` as keyword arguments. So the
              below two cases are the same.
  
              >>> l = L.Deconvolution2D(None, 7, 4, 2, 1)
              >>> y = l(x)
              >>> y.shape
              (1, 7, 20, 20)
  
              >>> l = L.Deconvolution2D(7, 4, stride=2, pad=1)
              >>> y = l(x)
              >>> y.shape
              (1, 7, 20, 20)
  
      """
  
      def __init__(self, in_channels, out_channels, ksize=None, stride=1, pad=0,
                   nobias=False, outsize=None, initialW=None, initial_bias=None,
                   **kwargs):
          super(Deconvolution2D, self).__init__()
  
          groups, = argument.parse_kwargs(
              kwargs, ('groups', 1),
              deterministic="deterministic argument is not supported anymore. "
              "Use chainer.using_config('cudnn_deterministic', value) "
              "context where value is either `True` or `False`.")
  
          if ksize is None:
              out_channels, ksize, in_channels = in_channels, out_channels, None
  
          self.ksize = ksize
          self.stride = _pair(stride)
          self.pad = _pair(pad)
          self.outsize = (None, None) if outsize is None else outsize
          self.out_channels = out_channels
          self.groups = int(groups)
  
          with self.init_scope():
              W_initializer = initializers._get_initializer(initialW)
              self.W = variable.Parameter(W_initializer)
              if in_channels is not None:
                  self._initialize_params(in_channels)
  
              if nobias:
                  self.b = None
              else:
                  if isinstance(initial_bias, (numpy.ndarray, cuda.ndarray)):
                      assert initial_bias.shape == (out_channels,)
                  if initial_bias is None:
                      initial_bias = 0
                  bias_initializer = initializers._get_initializer(initial_bias)
                  self.b = variable.Parameter(bias_initializer, out_channels)
  
      def _initialize_params(self, in_channels):
          kh, kw = _pair(self.ksize)
          if self.out_channels % self.groups != 0:
              raise ValueError('the number of output channels must be'
                               'divisible by the number of groups')
          if in_channels % self.groups != 0:
              raise ValueError('the number of input channels must be'
                               'divisible by the number of groups')
          W_shape = (in_channels, int(self.out_channels / self.groups), kh, kw)
          self.W.initialize(W_shape)
  
      def forward(self, x):
          if self.W.array is None:
              self._initialize_params(x.shape[1])
          return deconvolution_2d.deconvolution_2d(
              x, self.W, self.b, self.stride, self.pad, self.outsize,
              groups=self.groups)
  
  
  def _pair(x):
      if hasattr(x, '__getitem__'):
          return x
      return x, x
  