code: def _tile(cls, op: "IsMultilabel"):
      y = op.y
  
      if not isinstance(y, ENTITY_TYPE):
          return sklearn_is_multilabel(y)
  
      ctx = get_context()
  
      if has_unknown_shape(y):  # pragma: no cover
          yield
  
      if not (hasattr(y, "shape") and y.ndim == 2 and y.shape[1] > 1):
          return False
  
      labels = yield from recursive_tile(mt.unique(y))
      yield labels.chunks + [labels]
  
      if len(labels) < 3:
          if y.dtype.kind in "biu":
              return True
          if y.dtype.kind == "f":
              is_integral_float = yield from recursive_tile(
                  mt.all(mt.equal(y.astype(int), y))
              )
              yield is_integral_float.chunks
              is_integral_float = ctx.get_chunks_result(
                  [is_integral_float.chunks[0].key]
              )[0]
              if is_integral_float:
                  return True
  
      return False
  
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 the IsMultilabel obj's underlying member method code with example usage: obj._tile(cls, op) and returns: False, sklearn_is_multilabel, False, True, True