code: def predict(
      self,
      texts,
      target_words=None,
      n=1,
      external_memory=None,
      include_punctuation=False,
  ):
      # Prepare inputs
      input_idxes = [self.tokenizer.encode(text) for text in texts]
      if target_words is None:
          target_words = [None] * len(input_idxes)
          # target_words = [t.replace(self.SUBWORD_PREFIX, '') for t in target_words if t]
  
      # Pad token
      max_token_size = max([len(t) for t in input_idxes])
      for i, token_input in enumerate(input_idxes):
          for _ in range(max_token_size - len(token_input)):
              input_idxes[i].append(self.pad_id)
  
      target_poses = []
      if (
          external_memory is None
      ):  # First step or does not enable optimization
          for i, tokens in enumerate(input_idxes):
              target_poses.append(
                  len(self.padding_text_idxes)
                  + tokens.index(self.mask_id)
              )
              input_idxes[i] = self.padding_text_idxes + tokens
      else:
          for i, tokens in enumerate(input_idxes):
              target_poses.append(tokens.index(self.mask_id))
  
      perm_masks = torch.zeros(
          (len(input_idxes), len(input_idxes[0]), len(input_idxes[0])),
          dtype=torch.float,
      )
      target_mappings = torch.zeros(
          (len(input_idxes), 1, len(input_idxes[0])), dtype=torch.float
      )
      for i, target_pos in enumerate(target_poses):
          perm_masks[i][:, target_pos] = 1.0  # Mask the target word
          target_mappings[i, 0, target_pos] = 1.0
  
      # Convert to feature
      input_idxes = torch.tensor(input_idxes).to(self.device)
      perm_masks = perm_masks.to(self.device)
      target_mappings = target_mappings.to(self.device)
  
      # Prediction
      results = []
      with torch.no_grad():
          outputs = self.model(
              input_ids=input_idxes,
              perm_mask=perm_masks,
              target_mapping=target_mappings,
              mems=external_memory,
          )
  
      # Selection
      for output, target_token in zip(outputs[0], target_words):
          target_token_logits = output[0]
  
          seed = {
              "temperature": self.temperature,
              "top_k": self.top_k,
              "top_p": self.top_p,
          }
          target_token_logits = self.control_randomness(
              target_token_logits, seed
          )
          target_token_logits, target_token_idxes = self.filtering(
              target_token_logits, seed
          )
          if len(target_token_idxes) != 0:
              new_tokens = self.pick(
                  target_token_logits,
                  target_token_idxes,
                  target_word=target_token,
                  n=10,
                  include_punctuation=include_punctuation,
              )
              results.append([t[0] for t in new_tokens])
          else:
              results.append([""])
  
      return results
  
file_path: nlpaug/nlpaug/model/lang_models/xlnet.py
imports: import logging
  from nlpaug.model.lang_models import LanguageModels
  from nlpaug.util.selection.filtering import *
label_desc: Use image node_id 9 for calling the XlNet obj's underlying member method code with example usage: obj.predict(texts, target_words, n, external_memory, include_punctuation) and returns: results