code: def trim_mean(a, proportiontocut, axis=0):
      """Return mean of array after trimming distribution from both tails.
  
      If `proportiontocut` = 0.1, slices off 'leftmost' and 'rightmost' 10% of
      scores. The input is sorted before slicing. Slices off less if proportion
      results in a non-integer slice index (i.e., conservatively slices off
      `proportiontocut` ).
  
      Parameters
      ----------
      a : cupy.ndarray
          Input array.
      proportiontocut : float
          Fraction to cut off of both tails of the distribution.
      axis : int or None, optional
          Axis along which the trimmed means are computed. Default is 0.
          If None, compute over the whole array `a`.
  
      Returns
      -------
      trim_mean : ndarray
          Mean of trimmed array.
  
      See Also
      --------
      trimboth
      tmean : Compute the trimmed mean ignoring values outside given `limits`.
  
      Examples
      --------
      >>> import cupy as cp
      >>> from cupyx.scipy import stats
      >>> x = cp.arange(20)
      >>> stats.trim_mean(x, 0.1)
      array(9.5)
      >>> x2 = x.reshape(5, 4)
      >>> x2
      array([[ 0,  1,  2,  3],
             [ 4,  5,  6,  7],
             [ 8,  9, 10, 11],
             [12, 13, 14, 15],
             [16, 17, 18, 19]])
      >>> stats.trim_mean(x2, 0.25)
      array([ 8.,  9., 10., 11.])
      >>> stats.trim_mean(x2, 0.25, axis=1)
      array([ 1.5,  5.5,  9.5, 13.5, 17.5])
      """
      if a.size == 0:
          return cp.nan
  
      if axis is None:
          a = a.ravel()
          axis = 0
  
      nobs = a.shape[axis]
      lowercut = int(proportiontocut * nobs)
      uppercut = nobs - lowercut
      if lowercut > uppercut:
          raise ValueError("Proportion too big.")
  
      atmp = cp.partition(a, (lowercut, uppercut - 1), axis)
  
      sl = [slice(None)] * atmp.ndim
      sl[axis] = slice(lowercut, uppercut)
      return cp.mean(atmp[tuple(sl)], axis=axis)
  
label_desc: Use image node_id 1 for calling a global function with example usage: trim_mean(a, proportiontocut, axis) and returns: cp, cp