code: def tile(cls, op):
      # make sure type_true executed first
      chunks = [op.type_true.chunks[0]]
      yield chunks
  
      ctx = get_context()
      type_true = ctx.get_chunks_result([chunks[0].key])[0]
  
      y_true, y_pred = op.y_true, op.y_pred
      if type_true.item().startswith("multilabel"):
          differing_labels = mt.count_nonzero(y_true - y_pred, axis=1)
          score = mt.equal(differing_labels, 0)
      else:
          score = mt.equal(y_true, y_pred)
  
      result = _weighted_sum(score, op.sample_weight, op.normalize)
      return [(yield from recursive_tile(result))]
  
imports: import warnings
  import numpy
  from sklearn.exceptions import UndefinedMetricWarning
  from ...None import execute, fetch
  from ...None import opcodes
  from ...None import tensor
  from ...core import ENTITY_TYPE, recursive_tile
  from ...core.context import get_context
  from ...serialization.serializables import AnyField, BoolField, KeyField
  from ...tensor.core import TensorOrder
  from ..operands import LearnOperand, LearnOperandMixin, OutputType
  from ..preprocessing import LabelBinarizer, LabelEncoder
  from ..utils import check_array, check_consistent_length, column_or_1d
  from ..utils.multiclass import unique_labels
  from ..utils.sparsefuncs import count_nonzero
  from ._check_targets import _check_targets
label_desc: Use image node_id 9 for calling the AccuracyScore obj's underlying member method code with example usage: obj.tile(cls, op) and returns: list