code: def pruned_convolution(
      inp,
      outmaps,
      kernel,
      pad=None,
      stride=None,
      dilation=None,
      group=1,
      channel_last=False,
      w_init=None,
      b_init=None,
      base_axis=1,
      fix_parameters=False,
      rng=None,
      with_bias=True,
      prune_w=True,
      rate_w=0.9,
      prune_b=True,
      rate_b=0.9,
  ):
      """Pruned Convolution.
  
      Pruned Convolution is the convolution function,
      except the definition of the inner product is modified.
      The input-output relation of this function is as follows:
  
      .. math::
  
          y_{n, a, b} = \sum_{m} \sum_{i} \sum_{j} Q(w_{n, m, i, j}) x_{m, a + i, b + j},
  
      where :math:`Q(w_{ji})` is the pruning function, i.e., `F.prune`.
  
      .. note::
  
          1) if you would like to share weights between some layers, please
          make sure to share the standard, floating value weights (`weight`)
          and not the quantized weights (`quantized weight`)
  
          2) The weights and the quantized weights become synced only after :func:`~nnabla._variable.Variable.forward` is called,
          and not after a call to :func:`~nnabla._variable.Variable.backward`.
          To access the parameters of the network, remember to call :func:`~nnabla._variable.Variable.forward` once before doing so, otherwise the
          float weights and the quantized weights will not be in sync.
  
          3) CPU and GPU implementations now use float value for `quantized weight`,
          since this function is only for simulation purposes.
  
      Args:
          inp (~nnabla.Variable): N-D array.
          outmaps (int): Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
          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.
          group (int): Number of groups of channels. This makes connections across channels more sparse by grouping connections along map direction.
          w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for weight.
          b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for bias.
          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.
          prune_w (bool): Quantize weights if `True`.
          rate_w (float): Pruning rate for weights.
          prune_b (bool): Quantize bias if `True`.
          rate_b (float): Pruning rate for bias.
  
      Returns:
          :class:`~nnabla.Variable`: N-D array.
  
      """
      if channel_last:
          channels = inp.shape[-1]
          filter_shape = tuple(kernel) + (channels // group,)
      else:
          channels = inp.shape[base_axis]
          filter_shape = (channels // group,) + tuple(kernel)
      if w_init is None:
          w_init = UniformInitializer(
              calc_uniform_lim_glorot(channels, outmaps, tuple(kernel)),
              rng=rng,
          )
      if with_bias and b_init is None:
          b_init = ConstantInitializer()
  
      # Floating Weight
      w = get_parameter_or_create(
          "W",
          (outmaps,) + filter_shape,
          w_init,
          True,
          not fix_parameters,
      )
  
      # Quantized Weight
      if prune_w:
          w_q = get_parameter_or_create(
              "W_q",
              (outmaps, inp.shape[base_axis] // group) + tuple(kernel),
              w_init,
              False,
          )
          # Link computation graph
          real_w_q = F.prune(w, rate=rate_w, outputs=[w_q.data])
          real_w_q.persistent = True
      else:
          real_w_q = w
  
      # Bias
      # Floating
      real_b_q = None
      if with_bias:
          b = get_parameter_or_create(
              "b", (outmaps,), b_init, True, not fix_parameters
          )
          if prune_b:
              b_q = get_parameter_or_create(
                  "b_q", (outmaps,), b_init, False
              )
              # Link computation graph
              real_b_q = F.prune(b, rate=rate_b, outputs=[b_q.data])
              real_b_q.persistent = True
          else:
              real_b_q = b
  
      return F.convolution(
          inp,
          real_w_q,
          real_b_q,
          base_axis,
          pad,
          stride,
          dilation,
          group,
          channel_last,
      )
  
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 34 for calling a global function with example usage: pruned_convolution(inp, outmaps, kernel, pad, stride, dilation, group, channel_last, w_init, b_init, base_axis, fix_parameters, rng, with_bias, prune_w, rate_w, prune_b, rate_b) and returns: F