code: def main(args):
      if args.dataset == "mr":
          data, label = dataloader.read_MR(args.path)
      elif args.dataset == "subj":
          data, label = dataloader.read_SUBJ(args.path)
      elif args.dataset == "cr":
          data, label = dataloader.read_CR(args.path)
      elif args.dataset == "mpqa":
          data, label = dataloader.read_MPQA(args.path)
      elif args.dataset == "trec":
          train_x, train_y, test_x, test_y = dataloader.read_TREC(
              args.path
          )
          data = train_x + test_x
          label = None
      elif args.dataset == "sst":
          (
              train_x,
              train_y,
              valid_x,
              valid_y,
              test_x,
              test_y,
          ) = dataloader.read_SST(args.path)
          data = train_x + valid_x + test_x
          label = None
      else:
          raise Exception("unknown dataset: {}".format(args.dataset))
  
      emb_layer = modules.EmbeddingLayer(
          args.d, data, embs=dataloader.load_embedding(args.embedding)
      )
  
      if args.dataset == "trec":
          train_x, train_y, valid_x, valid_y = dataloader.cv_split2(
              train_x, train_y, nfold=10, valid_id=args.cv
          )
      elif args.dataset != "sst":
          (
              train_x,
              train_y,
              valid_x,
              valid_y,
              test_x,
              test_y,
          ) = dataloader.cv_split(
              data, label, nfold=10, test_id=args.cv
          )
  
      nclasses = max(train_y) + 1
  
      train_x, train_y = dataloader.create_batches(
          train_x,
          train_y,
          args.batch_size,
          emb_layer.word2id,
      )
      valid_x, valid_y = dataloader.create_batches(
          valid_x,
          valid_y,
          args.batch_size,
          emb_layer.word2id,
      )
      test_x, test_y = dataloader.create_batches(
          test_x,
          test_y,
          args.batch_size,
          emb_layer.word2id,
      )
  
      model = Model(args, emb_layer, nclasses).cuda()
      need_grad = lambda x: x.requires_grad
      optimizer = optim.Adam(
          filter(need_grad, model.parameters()), lr=args.lr
      )
      print(model)
  
      best_valid = 1e8
      test_err = 1e8
      for epoch in range(args.max_epoch):
          best_valid, test_err = train_model(
              epoch,
              model,
              optimizer,
              train_x,
              train_y,
              valid_x,
              valid_y,
              test_x,
              test_y,
              best_valid,
              test_err,
          )
          if args.lr_decay > 0:
              optimizer.param_groups[0]["lr"] *= args.lr_decay
  
      sys.stdout.write("best_valid: {:.6f}\n".format(best_valid))
      sys.stdout.write("test_err: {:.6f}\n".format(test_err))
  
imports: import os
  import sys
  import argparse
  import time
  import random
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.autograd import Variable
  from sru import *
  import dataloader
  import modules
label_desc: Use image node_id 3 for calling a global function with example usage: main(args) without return types