code: def train(
      self,
      train_set,
      val_set,
      max_iter,
      learning_rate,
      optimizer,
      train_grad_clip,
      train_valid_interval,
      train_early_stopping_patience,
      train_min_learning_rate,
      train_decay_patience,
      train_lr_decay_factor,
  ):
      # Prepare data for training
      (
          nd_positive_rating_values,
          train_dec_graph,
          valid_enc_graph,
          valid_dec_graph,
          train_labels,
          train_truths,
          valid_truths,
      ) = self._prepare_data(train_set, val_set)
  
      optimizer = get_optimizer(optimizer)(
          self.net.parameters(), lr=learning_rate
      )
      rating_loss_net = nn.CrossEntropyLoss()
  
      self._train_model(
          self.rating_values,
          train_dec_graph,
          valid_enc_graph,
          valid_dec_graph,
          train_labels,
          train_truths,
          valid_truths,
          nd_positive_rating_values,
          rating_loss_net,
          max_iter,
          optimizer,
          learning_rate,
          train_grad_clip,
          train_valid_interval,
          train_early_stopping_patience,
          train_min_learning_rate,
          train_decay_patience,
          train_lr_decay_factor,
      )
  
imports: import logging
  import time
  from collections import defaultdict
  import torch
  from torch import nn
  import dgl
  import numpy
  import scipy.sparse
  from tqdm import tqdm
  from .nn_modules import NeuralNetwork
  from .utils import get_optimizer, torch_net_info, torch_total_param_num
label_desc: Use image node_id 8 for calling the Model obj's underlying member method code with example usage: obj.train(train_set, val_set, max_iter, learning_rate, optimizer, train_grad_clip, train_valid_interval, train_early_stopping_patience, train_min_learning_rate, train_decay_patience, train_lr_decay_factor) without return types