code: def predict(self, texts, target_words=None, n=1):
      results = []
      # Prepare inputs
      for i in range(0, len(texts), self.batch_size):
          token_inputs = [
              self.tokenizer.encode(text)
              for text in texts[i : i + self.batch_size]
          ]
          if target_words is None:
              target_words = [None] * len(token_inputs)
  
          # Pad token
          max_token_size = max([len(t) for t in token_inputs])
          for i, token_input in enumerate(token_inputs):
              for _ in range(max_token_size - len(token_input)):
                  token_inputs[i].append(self.pad_id)
  
          target_poses = []
          for tokens in token_inputs:
              target_poses.append(tokens.index(self.mask_id))
          segment_inputs = [
              [0] * len(tokens) for tokens in token_inputs
          ]
          mask_inputs = [
              [1] * len(tokens) for tokens in token_inputs
          ]  # 1: real token, 0: padding token
  
          # Convert to feature
          token_inputs = torch.tensor(token_inputs).to(self.device)
          segment_inputs = torch.tensor(segment_inputs).to(self.device)
          mask_inputs = torch.tensor(mask_inputs).to(self.device)
  
          # Prediction
          results = []
          with torch.no_grad():
              outputs = self.model(
                  input_ids=token_inputs,
                  token_type_ids=segment_inputs,
                  attention_mask=mask_inputs,
              )
  
          # Selection
          for output, target_pos, target_token in zip(
              outputs[0], target_poses, target_words
          ):
              target_token_logits = output[target_pos]
  
              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,
                  )
                  results.append(
                      [
                          t[0]
                          for t in new_tokens
                          if self.get_subword_prefix() in t[0]
                      ]
                  )
              else:
                  results.append([""])
  
      return results
  
file_path: nlpaug/nlpaug/model/lang_models/roberta.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 Roberta obj's underlying member method code with example usage: obj.predict(texts, target_words, n) and returns: results