code: def sync_batch_normalization(
      inp,
      comm,
      group="world",
      axes=[1],
      decay_rate=0.9,
      eps=1e-5,
      batch_stat=True,
      output_stat=False,
      fix_parameters=False,
      param_init=None,
      no_scale=False,
      no_bias=False,
  ):
      """
      Synchronized batch normalization layer.
  
      For some tasks (e.g., semantic segmentation), batch size will be too small and BatchNormalization layer might not work well.
      SyncBatchNorlization layer solves these problems by synchronizing batch stats (mean and var) between multiple processes.
  
      .. math::
  
          \\begin{array}{lcl}
          \\mu &=& \\frac{1}{M} \\sum x_i\\\\
          \\sigma^2 &=& \\frac{1}{M} \\left(\\sum x_i - \\mu\\right)^2\\\\
          \\hat{x}_i &=& \\frac{x_i - \\mu}{\\sqrt{\\sigma^2 + \\epsilon }}\\\\
          y_i &= & \\hat{x}_i \\gamma + \\beta.
          \\end{array}
  
      where :math:`x_i, y_i` are the inputs.
  
      Args:
          inp (~nnabla.Variable): N-D array of input.
          comm (~nnabla.communicators.Communicator): The communicator
          group (string): The name of the communicator group
          axes (:obj:`tuple` of :obj:`int`):
              Mean and variance for each element in ``axes`` are calculated using
              elements on the rest axes. For example, if an input is 4 dimensions,
              and ``axes`` is ``[1]``,  batch mean is calculated as
              ``np.mean(inp.d, axis=(0, 2, 3), keepdims=True)``
              (using numpy expression as an example).
          decay_rate (float): Decay rate of running mean and variance.
          eps (float): Tiny value to avoid zero division by std.
          batch_stat (bool): Use mini-batch statistics rather than running ones.
          output_stat (bool): Output batch 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 ``'beta'``, ``'gamma'``, ``'mean'`` or ``'var'``.
              A value of the dict must be an :obj:`~nnabla.initializer.Initializer`
              or a :obj:`numpy.ndarray`.
              E.g. ``{'beta': ConstantInitializer(0), 'gamma': np.ones(gamma_shape) * 2}``.
          no_scale (bool): If `True`, the scale term is omitted.
          no_bias (bool): If `True`, the bias term is omitted.
  
      Returns:
          :class:`~nnabla.Variable`: N-D array.
  
      References:
          - Ioffe and Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, https://arxiv.org/abs/1502.03167
          - Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal, Context Encoding for Semantic Segmentation, https://arxiv.org/abs/1803.08904 
          - Implementing Synchronized Multi-GPU Batch Normalization https://hangzhang.org/PyTorch-Encoding/notes/syncbn.html
  
      The shape of parameters has the same number of dimensions with the input
      data, and the shapes in ``axes`` has the same dimensions with the input, while the rest has ``1``.
      If an input is 4-dim and ``axes=[1]``, the parameter shape will be
      ``param_shape  = np.mean(inp.d, axis=(0, 2, 3), keepdims=True).shape``
      (using numpy expression as an example).
  
      """
      from .normalization_functions import _init_beta_gamma
  
      shape_stat = [1 for _ in inp.shape]
      for i in range(len(axes)):
          shape_stat[axes[i]] = inp.shape[axes[i]]
  
      if param_init is None:
          param_init = {}
  
      beta, gamma = _init_beta_gamma(
          shape_stat, fix_parameters, param_init, no_bias, no_scale
      )
  
      mean_init = param_init.get("mean", ConstantInitializer(0))
      var_init = param_init.get("var", ConstantInitializer(1))
      mean = get_parameter_or_create(
          "mean", shape_stat, mean_init, False
      )
      var = get_parameter_or_create("var", shape_stat, var_init, False)
      return F.sync_batch_normalization(
          inp,
          beta,
          gamma,
          mean,
          var,
          comm,
          group,
          axes,
          decay_rate,
          eps,
          batch_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 22 for calling a global function with example usage: sync_batch_normalization(inp, comm, group, axes, decay_rate, eps, batch_stat, output_stat, fix_parameters, param_init, no_scale, no_bias) and returns: F