code: def pow2_quantized_convolution(
      inp,
      outmaps,
      kernel,
      pad=None,
      stride=None,
      dilation=None,
      group=1,
      w_init=None,
      b_init=None,
      base_axis=1,
      fix_parameters=False,
      rng=None,
      with_bias=True,
      quantize_w=True,
      with_zero_w=False,
      sign_w=True,
      n_w=8,
      m_w=2,
      ste_fine_grained_w=True,
      quantize_b=True,
      with_zero_b=False,
      sign_b=True,
      n_b=8,
      m_b=2,
      ste_fine_grained_b=True,
  ):
      """Pow2 Quantized Convolution.
  
      Pow2 Quantized 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_{n, m, i, j})` is the power-of-2 quantization function.
  
      .. 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) Quantized values are stored as floating point number 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. 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.
          quantize_w (bool): Quantize weights if `True`.
          sign_w (bool): Use signed quantization if `True`.
          n_w (int): Bit width used for weight.
          m_w (int): :math:`2^m` is upper bound and :math:`-2^m` is lower bound for weights. Default is 2.
          ste_fine_grained_w (bool): STE is fine-grained if `True`.
          quantize_b (bool): Quantize bias if `True`.
          sign_b (bool): Use signed quantization if `True`.
          n_b (int): Bit width used for bias.
          m_b (int): :math:`2^m` is upper bound and :math:`-2^m` is lower bound for bias. Default is 2.
          ste_fine_grained_b (bool): STE is fine-grained if `True`.
  
      Returns:
          :class:`~nnabla.Variable`: N-D array.
  
      """
      if w_init is None:
          w_init = UniformInitializer(
              calc_uniform_lim_glorot(
                  inp.shape[base_axis], 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, inp.shape[base_axis] // group) + tuple(kernel),
          w_init,
          True,
          not fix_parameters,
      )
  
      # Quantized Weight
      if quantize_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.pow2_quantize(
              w,
              quantize=quantize_w,
              sign=sign_w,
              with_zero=with_zero_w,
              n=n_w,
              m=m_w,
              ste_fine_grained=ste_fine_grained_w,
              outputs=[w_q.data],
          )
          real_w_q.persistent = True
      else:
          real_w_q = w
  
      # Bias
      # Floating
      b = None
      b_q = None
      real_b_q = None
  
      if with_bias:
          b = get_parameter_or_create(
              "b", (outmaps,), b_init, True, not fix_parameters
          )
          if quantize_b:
              b_q = get_parameter_or_create(
                  "b_q", (outmaps,), b_init, False
              )
              # Link computation graph
              real_b_q = F.pow2_quantize(
                  b,
                  quantize=quantize_b,
                  sign=sign_b,
                  with_zero=with_zero_b,
                  n=n_b,
                  m=m_b,
                  ste_fine_grained=ste_fine_grained_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,
      )
  
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 32 for calling a global function with example usage: pow2_quantized_convolution(inp, outmaps, kernel, pad, stride, dilation, group, w_init, b_init, base_axis, fix_parameters, rng, with_bias, quantize_w, with_zero_w, sign_w, n_w, m_w, ste_fine_grained_w, quantize_b, with_zero_b, sign_b, n_b, m_b, ste_fine_grained_b) and returns: F