class_docstr: The [`Evaluator`] class is the class from which all evaluators inherit. Refer to this class for methods shared across
  different evaluators.
  Base class implementing evaluator operations.
class_docstr_tok: ["The","[","`","Evaluator","`","]","class","is","the","class","from","which","all","evaluators","inherit",".","Refer","to","this","class","for","methods","shared","across","different","evaluators",".","Base","class","implementing","evaluator","operations","."]
docstr: A core method of the `Evaluator` class, which processes the pipeline outputs for compatibility with the metric.
docstr_tok: ["A","core","method","of","the","`","Evaluator","`","class",",","which","processes","the","pipeline","outputs","for","compatibility","with","the","metric","."]
code: def predictions_processor(self, *args, **kwargs):
      """
      A core method of the `Evaluator` class, which processes the pipeline outputs for compatibility with the metric.
      """
      raise NotImplementedError()
  
imports: from abc import ABC, abstractmethod
  from numbers import Number
  from typing import Any, Callable, Dict, List, Optional, Union
  from datasets import Dataset, load_dataset
  from evaluate.evaluator.utils import choose_split
  from time import perf_counter
  from typing_extensions import Literal
  from ..loading import load
  from ..module import EvaluationModule
  from ..utils.logging import get_logger
  from .utils import DatasetColumn
label_desc: Use image node_id 5 for calling the Evaluator obj's underlying member method code with example usage: obj.predictions_processor() without return types