code: def union1d(ar1, ar2, aggregate_size=None):
      """
      Find the union of two tensors.
  
      Return the unique, sorted tensor of values that are in either of the two
      input tensors.
  
      Parameters
      ----------
      ar1, ar2 : array_like
          Input tensors. They are flattened if they are not already 1D.
  
      Returns
      -------
      union1d : Tensor
          Unique, sorted union of the input tensors.
  
      Examples
      --------
      >>> import mars.tensor as mt
  
      >>> mt.union1d([-1, 0, 1], [-2, 0, 2]).execute()
      array([-2, -1,  0,  1,  2])
  
      To find the union of more than two arrays, use functools.reduce:
  
      >>> from functools import reduce
      >>> reduce(mt.union1d, ([1, 3, 4, 3], [3, 1, 2, 1], [6, 3, 4, 2])).execute()
      array([1, 2, 3, 4, 6])
      """
  
      from ..base import unique, sort
      from .concatenate import concatenate
  
      result = unique(
          concatenate((ar1, ar2), axis=None),
          aggregate_size=aggregate_size,
      )
      if aggregate_size == 1:
          return result
      # make sure the result is sorted
      # TODO(xuye.qin): remove when `mt.unique` supports sort shuffle
      return sort(result)
  
label_desc: Use image node_id 1 for calling a global function with example usage: union1d(ar1, ar2, aggregate_size) and returns: sort, result