class_docstr: Batch renormalization layer on outputs of linear or convolution functions.
  
  This link wraps the :func:`~chainer.functions.batch_renormalization` and
  :func:`~chainer.functions.fixed_batch_renormalization` functions.
  
  This is an extension of batch normalization, which ensures that the
  training and inference models generate the same outputs that depend on
  individual examples rather than the entire minibatch.
  
  See: `Batch Renormalization: Towards Reducing Minibatch Dependence in
  Batch-Normalized Models <https://arxiv.org/abs/1702.03275>`_
  
  .. seealso::
     :func:`~chainer.functions.batch_renormalization`,
     :func:`~chainer.functions.fixed_batch_renormalization`
     :func:`~chainer.functions.batch_normalization`,
code: def __init__(
      self,
      size,
      rmax=1,
      dmax=0,
      decay=0.9,
      eps=2e-5,
      dtype=None,
      use_gamma=True,
      use_beta=True,
      initial_gamma=None,
      initial_beta=None,
      initial_avg_mean=None,
      initial_avg_var=None,
  ):
      super(BatchRenormalization, self).__init__(
          size,
          decay,
          eps,
          dtype,
          use_gamma,
          use_beta,
          initial_gamma,
          initial_beta,
          initial_avg_mean,
          initial_avg_var,
      )
      self.rmax = rmax  # maximum allowed correction of variance
      self.dmax = dmax  # maximum allowed correction of mean
      self.r = None
      self.d = None
  
file_path: chainer/chainer/links/normalization/batch_renormalization.py
imports: import chainer
  from chainer import configuration
  from chainer.functions.normalization import batch_normalization
  from chainer.functions.normalization import batch_renormalization
  from chainer.links.normalization.batch_normalization import BatchNormalization
label_desc: Use image node_id 1 to create a new BatchRenormalization object from inherited base classes: BatchNormalization with example: obj = BatchRenormalization(size, rmax, dmax, decay, eps, dtype, use_gamma, use_beta, initial_gamma, initial_beta, initial_avg_mean, initial_avg_var)