class_docstr: An attack that greedily chooses from a list of possible perturbations in
  order of index, after ranking indices by importance.
  
  Args:
      wir_method: method for ranking most important words
      model_wrapper: model wrapper used for gradient-based ranking
class_docstr_tok: ["An","attack","that","greedily","chooses","from","a","list","of","possible","perturbations","in","order","of","index",",","after","ranking","indices","by","importance",".","Args",":","wir_method",":","method","for","ranking","most","important","words","model_wrapper",":","model","wrapper","used","for","gradient-based","ranking"]
code: def __init__(self, wir_method="unk", unk_token="[UNK]"):
      self.wir_method = wir_method
      self.unk_token = unk_token
  
file_path: TextAttack/textattack/search_methods/greedy_word_swap_wir.py
imports: import numpy
  import torch
  from torch.nn.functional import softmax
  from textattack.goal_function_results import GoalFunctionResultStatus
  from textattack.search_methods import SearchMethod
  from textattack.shared.validators import transformation_consists_of_word_swaps_and_deletions
label_desc: Use image node_id 1 to create a new GreedyWordSwapWIR object from inherited base classes: SearchMethod with example: obj = GreedyWordSwapWIR(wir_method, unk_token)