code: def _docs_normalization():
      _docs.set_doc(
          chainerx.batch_norm,
          """batch_norm(x, gamma, beta, running_mean, running_var, eps=2e-5, \
  decay=0.9, axis=None)
  Batch normalization function.
  
  It takes the input array ``x`` and two parameter arrays ``gamma`` and
  ``beta``. The parameter arrays must both have the same size.
  
  Args:
      x (~chainerx.ndarray): Input array.
      gamma (~chainerx.ndarray): Scaling parameter of normalized data.
      beta (~chainerx.ndarray): Shifting parameter of scaled normalized data.
      running_mean (~chainerx.ndarray):
          Running average of the mean. This is a running average of
          the mean over several mini-batches using the decay parameter.
          The function takes a previous running average, and updates
          the array in-place by the new running average.
      running_var (~chainerx.ndarray):
          Running average of the variance. This is a running average of
          the variance over several mini-batches using the decay parameter.
          The function takes a previous running average, and updates
          the array in-place by the new running average.
      eps (float): Epsilon value for numerical stability.
      decay (float): Decay rate of moving average. It is used during training.
      axis (int, tuple of int or None):
          Axis over which normalization is performed. When axis is ``None``,
          the first axis is treated as the batch axis and will be reduced
          during normalization.
  
  Note:
      During backpropagation, this function propagates the gradient of the
      output array to the input arrays ``x``, ``gamma`` and ``beta``.
  
  See: `Batch Normalization: Accelerating Deep Network Training by Reducing\
        Internal Covariate Shift <https://arxiv.org/abs/1502.03167>`_
  """,
      )
  
      _docs.set_doc(
          chainerx.fixed_batch_norm,
          """fixed_batch_norm(x, gamma, beta, mean, var, eps=2e-5, axis=None)
  Batch normalization function with fixed statistics.
  
  This is a variant of :func:`~chainerx.batch_norm`, where the mean
  and array statistics are given by the caller as fixed variables.
  
  Args:
      x (~chainerx.ndarray): Input array.
      gamma (~chainerx.ndarray): Scaling parameter of normalized data.
      beta (~chainerx.ndarray): Shifting parameter of scaled normalized data.
      mean (~chainerx.ndarray): Shifting parameter of input.
      var (~chainerx.ndarray): Square of scaling parameter of input.
      eps (float): Epsilon value for numerical stability.
      axis (int, tuple of int or None):
          Axis over which normalization is performed. When axis is ``None``,
          the first axis is treated as the batch axis and will be reduced
          during normalization.
  
  Note:
      During backpropagation, this function does not propagate gradients.
  """,
      )
  
file_path: chainer/chainerx/_docs/routines.py
imports: import chainerx
  from chainerx import _docs
label_desc: Use image node_id 13 for calling a global function with example usage: _docs_normalization() without return types