code: def _remove_one_token(
      instance: Instance,
      input_field_to_attack: str,
      grads: np.ndarray,
      ignore_tokens: List[str],
      beam_size: int,
      tag_mask: List[int],
  ) -> List[Tuple[Instance, int, List[int]]]:
      """
      Finds the token with the smallest gradient and removes it.
      """
      # Compute L2 norm of all grads.
      grads_mag = [np.sqrt(grad.dot(grad)) for grad in grads]
  
      # Skip all ignore_tokens by setting grad to infinity
      text_field: TextField = instance[input_field_to_attack]  # type: ignore
      for token_idx, token in enumerate(text_field.tokens):
          if token in ignore_tokens:
              grads_mag[token_idx] = float("inf")
  
      # For NER, skip all tokens that are not in outside
      if "tags" in instance:
          tag_field: SequenceLabelField = instance["tags"]  # type: ignore
          labels: List[str] = tag_field.labels  # type: ignore
          for idx, label in enumerate(labels):
              if label != "O":
                  grads_mag[idx] = float("inf")
      reduced_instances_and_smallest: List[
          Tuple[Instance, int, List[int]]
      ] = []
      for _ in range(beam_size):
          # copy instance and edit later
          copied_instance = deepcopy(instance)
          copied_text_field: TextField = copied_instance[input_field_to_attack]  # type: ignore
  
          # find smallest
          smallest = np.argmin(grads_mag)
          if grads_mag[smallest] == float(
              "inf"
          ):  # if all are ignored tokens, return.
              break
          grads_mag[smallest] = float(
              "inf"
          )  # so the other beams don't use this token
  
          # remove smallest
          inputs_before_smallest = copied_text_field.tokens[0:smallest]
          inputs_after_smallest = copied_text_field.tokens[
              smallest + 1 :
          ]
          copied_text_field.tokens = (
              inputs_before_smallest + inputs_after_smallest
          )
  
          if "tags" in instance:
              tag_field: SequenceLabelField = copied_instance["tags"]  # type: ignore
              tag_field_before_smallest = tag_field.labels[0:smallest]
              tag_field_after_smallest = tag_field.labels[
                  smallest + 1 :
              ]
              tag_field.labels = tag_field_before_smallest + tag_field_after_smallest  # type: ignore
              tag_field.sequence_field = copied_text_field
  
          copied_instance.indexed = False
          reduced_instances_and_smallest.append(
              (copied_instance, smallest, tag_mask)
          )
  
      return reduced_instances_and_smallest
  
file_path: allennlp/allennlp/interpret/attackers/input_reduction.py
imports: from copy import deepcopy
  from typing import List, Tuple
  import heapq
  import numpy
  import torch
  from allennlp.common.util import JsonDict, sanitize
  from allennlp.data import Instance
  from allennlp.data.fields import TextField, SequenceLabelField
  from allennlp.interpret.attackers import utils
  from allennlp.interpret.attackers.attacker import Attacker
  from allennlp.predictors import Predictor
label_desc: Use image node_id 1 for calling a global function with example usage: _remove_one_token(instance, input_field_to_attack, grads, ignore_tokens, beam_size, tag_mask) and returns: reduced_instances_and_smallest