code: def group_normalization(
      inp,
      num_groups,
      channel_axis=1,
      batch_axis=0,
      eps=1e-05,
      output_stat=False,
      fix_parameters=False,
      param_init=None,
      no_scale=False,
      no_bias=False,
  ):
      r"""
      Applies Group Normalization over an input tensor, which is defined as:
  
      .. math::
        \begin{eqnarray}
          \mu^g &=& \frac{1}{H} \sum_{i=1}^{H} x_i^g \\
          \sigma^g &=& \sqrt{\frac{1}{H} \sum_{i=1}^{H} \left(x_i^g - \mu^g\right)^2} \\
          y &=& \frac{x - \mu^g}{\sigma^g + \epsilon} \gamma + \beta
        \end{eqnarray}
  
      where :math:`x` and :math:`y` are input and output variable,
      :math:`\mu^g` and :math:`\sigma^g` are the mean and std of each group which contains `num_channels / num_groups` channels,
      and :math:`\gamma` and :math:`\beta` are adaptive gains and biases.
  
      The input channels, specified by :attr:`channel_axis`, are separeted into :attr:`num_groups` groups,
      and the mean and std are calculated over the each group.
      For example, if the input shape is [B, C, H, W] (= channel_axis=1, batch_axis=0),
      an input variable is once reshaped to [B, num_groups, C / num_groups, H, W]
      and standardize by its mean and std whose shapes are [B, num_groups, C / num_groups, 1, 1].
      Before returning, an output variable is reshaped again to the original input shape (= [B, C, H, W] in the case above).
  
      References:
  
          * `Yuxin Wu, Kaiming He, Group Normalization.
            <https://arxiv.org/abs/1803.08494>`_
  
      Args:
          inp (Variable): An input variable.
          num_groups (int): A number of groups. The channel dim of 'x' must be integer multiple of `num_groups`.
          channel_axis (int): Channel axis.
          batch_axis (int or repeated int): Axes mean and variance are taken.
          eps (float): Tiny value to avoid zero division by std.
          output_stat(bool): It true, the batch statistics of mean and variance.
          fix_parameters (bool): When set to `True`, the beta and gamma will not be updated.
          param_init (dict):
              Parameter initializers can be set with a dict. A key of the dict must
              be ``'gamma'``, ``'beta'``.
              A value of the dict must be an :obj:`~nnabla.initializer.Initializer`
              or a :obj:`numpy.ndarray`.
              E.g. ``{'gamma': np.ones(...) * 2, 'beta': ConstantInitializer(0)}``.
          no_scale (bool): If `True`, the scale term is omitted.
          no_bias (bool): If `True`, the bias term is omitted.
  
  
      Returns:
          * :obj:`~nnabla.Variable`: Normalized output variable.
          * :obj:`~nnabla.Variable`: Mean (if `output_stat=True`)
          * :obj:`~nnabla.Variable`: Std (if `output_stat=True`)
      """
      from nnabla.normalization_functions import (
          _force_list,
          _init_beta_gamma,
      )
  
      batch_axis = _force_list(batch_axis)
  
      shape_stat = [1 for _ in range(len(inp.shape))]
      shape_stat[channel_axis] = inp.shape[channel_axis]
  
      if param_init is None:
          param_init = {}
  
      beta, gamma = _init_beta_gamma(
          shape_stat, fix_parameters, param_init, no_bias, no_scale
      )
  
      # we dont have to broadcast beta and gamma here in this case because adaptive operation in bn is not used.
      return F.group_normalization(
          inp,
          beta,
          gamma,
          num_groups=num_groups,
          channel_axis=channel_axis,
          batch_axis=batch_axis,
          eps=eps,
          output_stat=output_stat,
      )
  
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 26 for calling a global function with example usage: group_normalization(inp, num_groups, channel_axis, batch_axis, eps, output_stat, fix_parameters, param_init, no_scale, no_bias) and returns: F