code: import argparse
  import json
  import os
  import pickle
  import sys
  import sagemaker_containers
  import pandas as pd
  import torch
  import torch.optim as optim
  import torch.utils.data
  
  from model import LSTMClassifier
  
  def model_fn(model_dir):
      """Load the PyTorch model from the `model_dir` directory."""
      print("Loading model.")
  
      # First, load the parameters used to create the model.
      model_info = {}
      model_info_path = os.path.join(model_dir, 'model_info.pth')
      with open(model_info_path, 'rb') as f:
          model_info = torch.load(f)
  
      print("model_info: {}".format(model_info))
  
      # Determine the device and construct the model.
      device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
      model = LSTMClassifier(model_info['embedding_dim'], model_info['hidden_dim'], model_info['vocab_size'])
  
      # Load the stored model parameters.
      model_path = os.path.join(model_dir, 'model.pth')
      with open(model_path, 'rb') as f:
          model.load_state_dict(torch.load(f))
  
      # Load the saved word_dict.
      word_dict_path = os.path.join(model_dir, 'word_dict.pkl')
      with open(word_dict_path, 'rb') as f:
          model.word_dict = pickle.load(f)
  
      model.to(device).eval()
  
      print("Done loading model.")
      return model
  
  def _get_train_data_loader(batch_size, training_dir):
      print("Get train data loader.")
  
      train_data = pd.read_csv(os.path.join(training_dir, "train.csv"), header=None, names=None)
  
      train_y = torch.from_numpy(train_data[[0]].values).float().squeeze()
      train_X = torch.from_numpy(train_data.drop([0], axis=1).values).long()
  
      train_ds = torch.utils.data.TensorDataset(train_X, train_y)
  
      return torch.utils.data.DataLoader(train_ds, batch_size=batch_size)
  
  
  def train(model, train_loader, epochs, optimizer, loss_fn, device):
      """
      This is the training method that is called by the PyTorch training script. The parameters
      passed are as follows:
      model        - The PyTorch model that we wish to train.
      train_loader - The PyTorch DataLoader that should be used during training.
      epochs       - The total number of epochs to train for.
      optimizer    - The optimizer to use during training.
      loss_fn      - The loss function used for training.
      device       - Where the model and data should be loaded (gpu or cpu).
      """
          
      for epoch in range(1, epochs + 1):
          model.train()
          total_loss = 0
          for batch in train_loader:         
              batch_X, batch_y = batch
              
              batch_X = batch_X.to(device)
              batch_y = batch_y.to(device)
              
              # TODO: Complete this train method to train the model provided.
              optimizer.zero_grad()
              prediction = model(batch_X)
              loss = loss_fn(prediction, batch_y)
              loss.backward()
              optimizer.step()
              
              total_loss += loss.data.item()
          print("Epoch: {}, BCELoss: {}".format(epoch, total_loss / len(train_loader)))
  
  
  if __name__ == '__main__':
      # All of the model parameters and training parameters are sent as arguments when the script
      # is executed. Here we set up an argument parser to easily access the parameters.
  
      parser = argparse.ArgumentParser()
  
      # Training Parameters
      parser.add_argument('--batch-size', type=int, default=512, metavar='N',
                          help='input batch size for training (default: 512)')
      parser.add_argument('--epochs', type=int, default=10, metavar='N',
                          help='number of epochs to train (default: 10)')
      parser.add_argument('--seed', type=int, default=1, metavar='S',
                          help='random seed (default: 1)')
  
      # Model Parameters
      parser.add_argument('--embedding_dim', type=int, default=32, metavar='N',
                          help='size of the word embeddings (default: 32)')
      parser.add_argument('--hidden_dim', type=int, default=100, metavar='N',
                          help='size of the hidden dimension (default: 100)')
      parser.add_argument('--vocab_size', type=int, default=5000, metavar='N',
                          help='size of the vocabulary (default: 5000)')
  
      # SageMaker Parameters
      parser.add_argument('--hosts', type=list, default=json.loads(os.environ['SM_HOSTS']))
      parser.add_argument('--current-host', type=str, default=os.environ['SM_CURRENT_HOST'])
      parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
      parser.add_argument('--data-dir', type=str, default=os.environ['SM_CHANNEL_TRAINING'])
      parser.add_argument('--num-gpus', type=int, default=os.environ['SM_NUM_GPUS'])
  
      args = parser.parse_args()
  
      device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
      print("Using device {}.".format(device))
  
      torch.manual_seed(args.seed)
  
      # Load the training data.
      train_loader = _get_train_data_loader(args.batch_size, args.data_dir)
  
      # Build the model.
      model = LSTMClassifier(args.embedding_dim, args.hidden_dim, args.vocab_size).to(device)
  
      with open(os.path.join(args.data_dir, "word_dict.pkl"), "rb") as f:
          model.word_dict = pickle.load(f)
  
      print("Model loaded with embedding_dim {}, hidden_dim {}, vocab_size {}.".format(
          args.embedding_dim, args.hidden_dim, args.vocab_size
      ))
  
      # Train the model.
      optimizer = optim.Adam(model.parameters())
      loss_fn = torch.nn.BCELoss()
  
      train(model, train_loader, args.epochs, optimizer, loss_fn, device)
  
      # Save the parameters used to construct the model
      model_info_path = os.path.join(args.model_dir, 'model_info.pth')
      with open(model_info_path, 'wb') as f:
          model_info = {
              'embedding_dim': args.embedding_dim,
              'hidden_dim': args.hidden_dim,
              'vocab_size': args.vocab_size,
          }
          torch.save(model_info, f)
  
  	# Save the word_dict
      word_dict_path = os.path.join(args.model_dir, 'word_dict.pkl')
      with open(word_dict_path, 'wb') as f:
          pickle.dump(model.word_dict, f)
  
  	# Save the model parameters
      model_path = os.path.join(args.model_dir, 'model.pth')
      with open(model_path, 'wb') as f:
          torch.save(model.cpu().state_dict(), f)
  
apis: torch.utils.data.DataLoader torch.load torch.manual_seed torch.save torch.cuda.is_available torch.from_numpy torch.nn.BCELoss torch.utils.data.TensorDataset