class_docstr: a `Predictor` is a thin wrapper around an AllenNLP model that handles JSON -> JSON predictions
  that can be used for serving models through the web API or making predictions in bulk.
docstr: This function takes a model's outputs for an Instance, and it labels that instance according
  to the `outputs`. This function is used to (1) compute gradients of what the model predicted;
  (2) label the instance for the attack. For example, (a) for the untargeted attack for classification
  this function labels the instance according to the class with the highest probability; (b) for
  targeted attack, it directly constructs fields from the given target.
  The return type is a list because in some tasks there are multiple predictions in the output
  (e.g., in NER a model predicts multiple spans). In this case, each instance in the returned list of
  Instances contains an individual entity prediction as the label.
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code: def predictions_to_labeled_instances(
      self, instance: Instance, outputs: Dict[str, numpy.ndarray]
  ) -> List[Instance]:
      """
      This function takes a model's outputs for an Instance, and it labels that instance according
      to the `outputs`. This function is used to (1) compute gradients of what the model predicted;
      (2) label the instance for the attack. For example, (a) for the untargeted attack for classification
      this function labels the instance according to the class with the highest probability; (b) for
      targeted attack, it directly constructs fields from the given target.
      The return type is a list because in some tasks there are multiple predictions in the output
      (e.g., in NER a model predicts multiple spans). In this case, each instance in the returned list of
      Instances contains an individual entity prediction as the label.
      """
  
      raise RuntimeError(
          "implement this method for model interpretations or attacks"
      )
  
imports: from typing import List, Iterator, Dict, Tuple, Any, Type, Union, Optional
  import logging
  from os import PathLike
  import json
  import re
  from contextlib import contextmanager
  import numpy
  import torch
  from torch.utils.hooks import RemovableHandle
  from torch import Tensor
  from torch import backends
  from allennlp.common import Registrable, plugins
  from allennlp.common.util import JsonDict, sanitize
  from allennlp.data import DatasetReader, Instance
  from allennlp.data.batch import Batch
  from allennlp.models import Model
  from allennlp.models.archival import Archive, load_archive
  from allennlp.nn import util
label_desc: Use image node_id 12 for calling the Predictor obj's underlying member method code with example usage: obj.predictions_to_labeled_instances(instance, outputs) without return types