code: def deconvolution(
      inp,
      outmaps,
      kernel,
      pad=None,
      stride=None,
      dilation=None,
      group=1,
      channel_last=False,
      output_padding=None,
      w_init=None,
      b_init=None,
      base_axis=1,
      fix_parameters=False,
      rng=None,
      with_bias=True,
      apply_w=None,
      apply_b=None,
  ):
      """
      Deconvolution layer.
  
      Args:
          inp (~nnabla.Variable): N-D array.
          outmaps (int): Number of deconvolution kernels (which is equal to the number of output channels). For example, to apply deconvolution on an input with 16 types of filters, specify 16.
          kernel (:obj:`tuple` of :obj:`int`): Convolution kernel size. For example, to apply deconvolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5).
          pad (:obj:`tuple` of :obj:`int`): Padding sizes for dimensions.
          stride (:obj:`tuple` of :obj:`int`): Stride sizes for dimensions.
          dilation (:obj:`tuple` of :obj:`int`): Dilation sizes for dimensions.
          group (int): Number of groups of channels. This makes connections across channels sparser by grouping connections along map direction.
          w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for weight. By default, it is initialized with :obj:`nnabla.initializer.UniformInitializer` within the range determined by :obj:`nnabla.initializer.calc_uniform_lim_glorot`.
          b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for bias. By default, it is initialized with zeros if `with_bias` is `True`.
          base_axis (int): Dimensions up to `base_axis` are treated as the sample dimensions.
          fix_parameters (bool): When set to `True`, the weights and biases will not be updated.
          rng (numpy.random.RandomState): Random generator for Initializer.
          with_bias (bool): Specify whether to include the bias term.
          apply_w (function): Lambda, function, or callable object applied to the weights.
          apply_b (function): Lambda, function, or callable object applied to the bias.
  
      Returns:
          :class:`~nnabla.Variable`: N-D array. See :obj:`~nnabla.functions.deconvolution` for the output shape.
  
      """
      if channel_last:
          channels = inp.shape[-1]
          weights_shape = (
              (channels,) + tuple(kernel) + (outmaps // group,)
          )
      else:
          channels = inp.shape[base_axis]
          weights_shape = (
              channels,
              outmaps // group,
          ) + tuple(kernel)
      if w_init is None:
          w_init = UniformInitializer(
              calc_uniform_lim_glorot(outmaps, channels, tuple(kernel)),
              rng=rng,
          )
      if with_bias and b_init is None:
          b_init = ConstantInitializer()
      w = get_parameter_or_create(
          "W", weights_shape, w_init, True, not fix_parameters
      )
      if apply_w is not None:
          w = apply_w(w)
      b = None
      if with_bias:
          b = get_parameter_or_create(
              "b", (outmaps,), b_init, True, not fix_parameters
          )
          if apply_b is not None:
              b = apply_b(b)
      return F.deconvolution(
          inp,
          w,
          b,
          base_axis,
          pad,
          stride,
          dilation,
          group,
          channel_last,
          output_padding,
      )
  
imports: from six import exec_
  import numpy
  import nnabla
  import nnabla.functions
  from nnabla.parameter import get_parameter_or_create, get_parameter
  from nnabla.initializer import calc_uniform_lim_glorot, ConstantInitializer, NormalInitializer, UniformInitializer, WeightNormalizationScaleInitializer
label_desc: Use image node_id 15 for calling a global function with example usage: deconvolution(inp, outmaps, kernel, pad, stride, dilation, group, channel_last, output_padding, w_init, b_init, base_axis, fix_parameters, rng, with_bias, apply_w, apply_b) and returns: F