code: def depthwise_deconvolution(
      inp,
      kernel,
      pad=None,
      stride=None,
      dilation=None,
      divisor=1,
      w_init=None,
      b_init=None,
      base_axis=1,
      fix_parameters=False,
      rng=None,
      with_bias=True,
  ):
      """Depthwise deconvolution computes the transposed depthwise
      convolution for one-dimensional and two-dimensional input data.
  
      Args:
          inp (~nnabla.Variable): N-D array.
          kernel (:obj:`tuple` of :obj:`int`): Convolution kernel size. For example, to apply convolution 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.
          divisor (:obj:`int`): Number of input feature maps per output feature map.
          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.
  
      Returns:
          :class:`~nnabla.Variable`: N-D array. See :obj:`~nnabla.functions.depthwise_deconvolution` for the output shape.
  
      """
      if w_init is None:
          w_init = UniformInitializer(
              calc_uniform_lim_glorot(
                  inp.shape[base_axis],
                  inp.shape[base_axis],
                  tuple(kernel),
              ),
              rng=rng,
          )
      if with_bias and b_init is None:
          b_init = ConstantInitializer()
      w = get_parameter_or_create(
          "W",
          (inp.shape[base_axis],) + tuple(kernel),
          w_init,
          True,
          not fix_parameters,
      )
      b = None
      if with_bias:
          b = get_parameter_or_create(
              "b",
              (inp.shape[base_axis] // divisor,),
              b_init,
              True,
              not fix_parameters,
          )
      return F.depthwise_deconvolution(
          inp, w, b, base_axis, pad, stride, dilation, divisor
      )
  
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 16 for calling a global function with example usage: depthwise_deconvolution(inp, kernel, pad, stride, dilation, divisor, w_init, b_init, base_axis, fix_parameters, rng, with_bias) and returns: F