code: def mean_subtraction(
      x, mean, t, base_axis=1, update_running_mean=True
  ):
      r"""
      It subtracts the mean of the elements of the input array,
      and normalizes it to :math:`0`. Preprocessing arrays with this function has the effect of improving accuracy
      in various tasks such as image classification.
  
      At training time, this function is defined as
  
      .. math::
          \begin{eqnarray}
            \mu &=& \frac{1}{M} \sum x_i \\
            y_i &=& x_i - \mu
          \end{eqnarray}
  
      At testing time, the mean values used are those that were computed during training by moving average.
  
      Note:
          The backward performs an approximated differentiation that takes into account only the latest mini-batch.
  
      Args:
          x(~nnabla.Variable): N-D array of input.
          mean(~nnabla.Variable): N-D array of running mean (modified during forward execution).
          t(~nnabla.Variable): Scalar of num of iteration of running mean (modified during forward execution).
          base_axis(int): Base axis of Mean Subtraction operation. Dimensions up to base_axis is treated as sample dimension.
              [default= `1` ]
          update_running_mean(bool): Update running mean during forward execution.
              [default= `True` ]
  
      Returns:
          ~nnabla.Variable: N-D array.
  
      See Also:
          ``nnabla.function_bases.mean_subtraction``.
  
      """
      from .function_bases import (
          mean_subtraction as mean_subtraction_base,
      )
  
      return mean_subtraction_base(
          x,
          mean,
          t,
          base_axis=base_axis,
          update_running_mean=update_running_mean,
      )
  
imports: from __future__ import absolute_import
  from .function_bases import *
  from .None import dtypes
  import nnabla
  import numpy
  from .normalization_functions import *
  from .numpy_compat_functions import *
label_desc: Use image node_id 13 for calling a global function with example usage: mean_subtraction(x, mean, t, base_axis, update_running_mean) and returns: mean_subtraction_base