code: def execute(self, input_ids):
      output = self.embedding(input_ids)
      # TODO 去除padding
      output, (h, c) = self.lstm(output, self.init_hidden(output))
      # len, batch, hidden_size
      output = self.layer(output[-1])
  
      return output
  
imports: import os
  import sys
  import time
  from datasets import load_dataset
  from datasets import DatasetDict
  import jittor
  from jittor import nn, Module
  from jittor.dataset import Dataset
  from fastNLP.core.controllers.trainer import Trainer
  from fastNLP.core.metrics.accuracy import Accuracy
  from fastNLP.core.vocabulary import Vocabulary
  from fastNLP.core.callbacks.progress_callback import RichCallback
  from fastNLP.core.callbacks.callback import Callback
  from fastNLP.core.dataloaders.jittor_dataloader.fdl import JittorDataLoader
label_desc: Use image node_id 3 for calling the LSTM obj's underlying member method code with example usage: obj.execute(input_ids) and returns: output