code: def setxor1d(ar1, ar2, assume_unique=False):
      """
      Find the set exclusive-or of two arrays.
  
      Return the sorted, unique values that are in only one (not both) of the
      input arrays.
  
      Parameters
      ----------
      ar1, ar2 : array_like
          Input arrays.
      assume_unique : bool
          If True, the input arrays are both assumed to be unique, which
          can speed up the calculation.  Default is False.
  
      Returns
      -------
      setxor1d : ndarray
          Sorted 1D array of unique values that are in only one of the input
          arrays.
  
      Examples
      --------
      >>> a = np.array([1, 2, 3, 2, 4])
      >>> b = np.array([2, 3, 5, 7, 5])
      >>> np.setxor1d(a,b)
      array([1, 4, 5, 7])
  
      """
      if not assume_unique:
          ar1 = unique(ar1)
          ar2 = unique(ar2)
  
      aux = np.concatenate((ar1, ar2))
      if aux.size == 0:
          return aux
  
      aux.sort()
      flag = np.concatenate(([True], aux[1:] != aux[:-1], [True]))
      return aux[flag[1:] & flag[:-1]]
  
imports: import functools
  import warnings
  from typing import NamedTuple
  import numpy
  from numpy._core import overrides
label_desc: Use image node_id 18 for calling a global function with example usage: setxor1d(ar1, ar2, assume_unique) and returns: aux, aux