code: def unique_labels(*ys):
      """
      Extract an ordered array of unique labels.
  
      We don't allow:
          - mix of multilabel and multiclass (single label) targets
          - mix of label indicator matrix and anything else,
            because there are no explicit labels)
          - mix of label indicator matrices of different sizes
          - mix of string and integer labels
  
      At the moment, we also don't allow "multiclass-multioutput" input type.
  
      Parameters
      ----------
      *ys : array-likes
  
      Returns
      -------
      out : ndarray of shape (n_unique_labels,)
          An ordered array of unique labels.
  
      Examples
      --------
      >>> from mars.learn.utils.multiclass import unique_labels
      >>> unique_labels([3, 5, 5, 5, 7, 7]).execute()
      array([3, 5, 7])
      >>> unique_labels([1, 2, 3, 4], [2, 2, 3, 4]).execute()
      array([1, 2, 3, 4])
      >>> unique_labels([1, 2, 10], [5, 11]).execute()
      array([ 1,  2,  5, 10, 11])
      """
      if not ys:
          raise ValueError("No argument has been passed.")
  
      ys = list(ys)
      op = UniqueLabels(ys=ys)
      return op(ys)
  
imports: import itertools
  from collections.abc import Sequence
  from typing import List
  import numpy
  from scipy.sparse import spmatrix
  from sklearn.utils.multiclass import is_multilabel, type_of_target
  from ...None import opcodes
  from ...None import tensor
  from ...core import ENTITY_TYPE, TILEABLE_TYPE, recursive_tile
  from ...core.context import get_context
  from ...serialization.serializables import AnyField, ListField
  from ...tensor.core import TensorOrder, TENSOR_TYPE
  from ...typing import TileableType
  from ...utils import has_unknown_shape
  from ..operands import LearnOperand, LearnOperandMixin, OutputType
  from ..utils import assert_all_finite
  from .validation import check_array
label_desc: Use image node_id 3 for calling a global function with example usage: unique_labels() and returns: op