class_docstr: Transforms an input by replacing its words with visually similar words
  using homoglyph swaps.
  
  >>> from textattack.transformations import WordSwapHomoglyphSwap
  >>> from textattack.augmentation import Augmenter
  
  >>> transformation = WordSwapHomoglyphSwap()
  >>> augmenter = Augmenter(transformation=transformation)
  >>> s = 'I am fabulous.'
  >>> augmenter.augment(s)
docstr: Returns a list containing all possible words with 1 character
  replaced by a homoglyph.
docstr_tok: ["Returns","a","list","containing","all","possible","words","with","1","character","replaced","by","a","homoglyph","."]
code: def _get_replacement_words(self, word):
      """Returns a list containing all possible words with 1 character
      replaced by a homoglyph."""
      candidate_words = []
  
      if self.random_one:
          i = np.random.randint(0, len(word))
          if word[i] in self.homos:
              repl_letter = self.homos[word[i]]
              candidate_word = word[:i] + repl_letter + word[i + 1 :]
              candidate_words.append(candidate_word)
      else:
          for i in range(len(word)):
              if word[i] in self.homos:
                  repl_letter = self.homos[word[i]]
                  candidate_word = (
                      word[:i] + repl_letter + word[i + 1 :]
                  )
                  candidate_words.append(candidate_word)
  
      return candidate_words
  
file_path: TextAttack/textattack/transformations/word_swaps/word_swap_homoglyph_swap.py
imports: import numpy
  from .word_swap import WordSwap
label_desc: Use image node_id 2 for calling the WordSwapHomoglyphSwap obj's underlying member method code with example usage: obj._get_replacement_words(word) and returns: candidate_words