code: def _warn_prf(
      average, modifier, msg_start, result_size
  ):  # pragma: no cover
      axis0, axis1 = "sample", "label"
      if average == "samples":
          axis0, axis1 = axis1, axis0
      msg = (
          "{0} ill-defined and being set to 0.0 {{0}} "
          "no {1} {2}s. Use `zero_division` parameter to control"
          " this behavior.".format(msg_start, modifier, axis0)
      )
      if result_size == 1:
          msg = msg.format("due to")
      else:
          msg = msg.format("in {0}s with".format(axis1))
      warnings.warn(msg, UndefinedMetricWarning, stacklevel=2)
  
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 6 for calling a global function with example usage: _warn_prf(average, modifier, msg_start, result_size) without return types