code: def _check_zero_division(zero_division):  # pragma: no cover
      if isinstance(zero_division, str) and zero_division == "warn":
          return
      elif isinstance(
          zero_division, (int, float)
      ) and zero_division in [0, 1]:
          return
      raise ValueError(
          "Got zero_division={0}."
          ' Must be one of ["warn", 0, 1]'.format(zero_division)
      )
  
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 5 for calling a global function with example usage: _check_zero_division(zero_division) without return types