code: def trimmed_mean(
      a, limits=(0.1, 0.1), inclusive=(1, 1), relative=True, axis=None
  ):
      """Returns the trimmed mean of the data along the given axis.
  
      %s
  
      """ % trimdoc
      if (not isinstance(limits, tuple)) and isinstance(limits, float):
          limits = (limits, limits)
      if relative:
          return trimr(
              a, limits=limits, inclusive=inclusive, axis=axis
          ).mean(axis=axis)
      else:
          return trima(a, limits=limits, inclusive=inclusive).mean(
              axis=axis
          )
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from numpy import ndarray
  import numpy.ma
  from numpy.ma import masked, nomask
  from scipy._lib.six import iteritems
  import itertools
  import warnings
  from collections import namedtuple
  from .None import distributions
  import scipy.special
  from ._stats_mstats_common import _find_repeats, linregress, theilslopes, siegelslopes
label_desc: Use image node_id 31 for calling a global function with example usage: trimmed_mean(a, limits, inclusive, relative, axis) and returns: trimr, trima