code: def predict(self, batch, num_beams=4, max_length=50):
      self.eval()
      with torch.no_grad():
          outputs = self.model.generate(
              input_ids=batch.enc_idxs,
              attention_mask=batch.enc_attn,
              num_beams=num_beams,
              max_length=max_length,
          )
  
      final_output = []
      for bid in range(len(batch.enc_idxs)):
          output_sentence = self.tokenizer.decode(
              outputs[bid],
              skip_special_tokens=True,
              clean_up_tokenization_spaces=True,
          )
          # output_sentence = "Event" + output_sentence
          final_output.append(output_sentence)
      self.train()
  
      return final_output
  
file_path: DeepKE/src/deepke/event_extraction/standard/degree/model.py
imports: import logging
  import numpy
  import torch
  import torch.nn
  from transformers import AutoConfig, AutoModelForPreTraining
  import ipdb
label_desc: Use image node_id 3 for calling the GenerativeModel obj's underlying member method code with example usage: obj.predict(batch, num_beams, max_length) and returns: final_output