code: def __init__(self, num_of_words, hidden_size, features):
      self.embedding = nn.Embedding(num_of_words, features)
      self.lstm = nn.LSTM(features, hidden_size, batch_first=True)
      self.layer = nn.Linear(hidden_size, 2)
      self.softmax = nn.Softmax(dim=1)
      self.loss_fn = nn.CrossEntropyLoss()
  
      self.hidden_size = hidden_size
      self.features = features
  
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 1 to create a new LSTM object from inherited base classes: Module with example: obj = LSTM(num_of_words, hidden_size, features)