parameters: (data, gamma, beta, num_groups=1, eps=1e-3, output_mean_var=False)
return_statement: return list(out)
docstring: r"""Group normalization.
  
      The input channels are separated into ``num_groups`` groups,
      each containing ``num_channels / num_groups`` channels.
      The mean and standard-deviation are calculated separately over the each group.
  
      .. math::
  
        data = data.reshape((N, num_groups, C // num_groups, ...))
        out = \frac{data - mean(data, axis)}{\sqrt{var(data, axis) + \epsilon}} * gamma + beta
  
      Both ``gamma`` and ``beta`` are learnable parameters.
  
  
  
      Defined in ../src/operator/nn/group_norm.cc:L78
  
      Parameters
      ----------
      data : NDArray
          Input data
      gamma : NDArray
          gamma array
      beta : NDArray
          beta array
      num_groups : int, optional, default='1'
          Total number of groups.
      eps : float, optional, default=9.99999975e-06
          An `epsilon` parameter to prevent division by 0.
      output_mean_var : boolean, optional, default=0
          Output the mean and std calculated along the given axis.
  
      Returns
      -------
      out : NDArray or list of NDArrays
          The output of this function.
docstring_summary: r"""Group normalization.
docstring_tokens: r Group normalization .
function: def group_norm(data, gamma, beta, num_groups=1, eps=1e-3, output_mean_var=False):
      r"""Group normalization.
  
      The input channels are separated into ``num_groups`` groups,
      each containing ``num_channels / num_groups`` channels.
      The mean and standard-deviation are calculated separately over the each group.
  
      .. math::
  
        data = data.reshape((N, num_groups, C // num_groups, ...))
        out = \frac{data - mean(data, axis)}{\sqrt{var(data, axis) + \epsilon}} * gamma + beta
  
      Both ``gamma`` and ``beta`` are learnable parameters.
  
  
  
      Defined in ../src/operator/nn/group_norm.cc:L78
  
      Parameters
      ----------
      data : NDArray
          Input data
      gamma : NDArray
          gamma array
      beta : NDArray
          beta array
      num_groups : int, optional, default='1'
          Total number of groups.
      eps : float, optional, default=9.99999975e-06
          An `epsilon` parameter to prevent division by 0.
      output_mean_var : boolean, optional, default=0
          Output the mean and std calculated along the given axis.
  
      Returns
      -------
      out : NDArray or list of NDArrays
          The output of this function.
      """
      out = _api_internal.group_norm(data, gamma, beta, num_groups, eps, output_mean_var)
      if isinstance(out, NDArrayBase):
          return out
      return list(out)
function_tokens: def group_norm ( data , gamma , beta , num_groups = 1 , eps = 1e-3 , output_mean_var = False ) : out = _api_internal . group_norm ( data , gamma , beta , num_groups , eps , output_mean_var ) if isinstance ( out , NDArrayBase ) : return out return list ( out )