text: import logging
  from typing import Callable
  
  from tensorboardX import SummaryWriter
  import torch
  import torch.nn as nn
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
  from tqdm import tqdm
  
  from chemprop.args import TrainArgs
  from chemprop.data import MoleculeDataLoader, MoleculeDataset
  from chemprop.models import MoleculeModel
  from chemprop.nn_utils import compute_gnorm, compute_pnorm, NoamLR
  
  
  def train(model: MoleculeModel,
            data_loader: MoleculeDataLoader,
            loss_func: Callable,
            optimizer: Optimizer,
            scheduler: _LRScheduler,
            args: TrainArgs,
            n_iter: int = 0,
            logger: logging.Logger = None,
            writer: SummaryWriter = None) -> int:
      """
      Trains a model for an epoch.
  
      :param model: A :class:`~chemprop.models.model.MoleculeModel`.
      :param data_loader: A :class:`~chemprop.data.data.MoleculeDataLoader`.
      :param loss_func: Loss function.
      :param optimizer: An optimizer.
      :param scheduler: A learning rate scheduler.
      :param args: A :class:`~chemprop.args.TrainArgs` object containing arguments for training the model.
      :param n_iter: The number of iterations (training examples) trained on so far.
      :param logger: A logger for recording output.
      :param writer: A tensorboardX SummaryWriter.
      :return: The total number of iterations (training examples) trained on so far.
      """
      debug = logger.debug if logger is not None else print
      
      model.train()
      loss_sum, iter_count = 0, 0
  
      print("\n\nWe are training for one epoch!!!!\n\n")
      for batch in tqdm(data_loader, total=len(data_loader)):
  
          # Prepare batch
          batch: MoleculeDataset
  
  
       #   print("\nANISH: ", batch.smiles(), "size: ", len(batch.smiles()))
  
          #places = ['Berlin', 'Cape Town', 'Sydney', 'Moscow']
  
          with open('cursmiles.txt', 'w') as filehandle:
              
              filetmp = open('cursmiles.txt', 'r+')
              filetmp.truncate(0)
  
         #     print("\n\nANISH: The size of the ligand batch is ", len(batch.smiles()), "\n\n")
  
              for smilesstring in batch.smiles():
                  filehandle.write('%s\n' % smilesstring)
  
          mol_batch, features_batch, target_batch = batch.batch_graph(), batch.features(), batch.targets()
          mask = torch.Tensor([[x is not None for x in tb] for tb in target_batch])
          targets = torch.Tensor([[0 if x is None else x for x in tb] for tb in target_batch])
  
          # Run model
          model.zero_grad()
          preds = model(mol_batch, features_batch)
  
          # Move tensors to correct device
          mask = mask.to(preds.device)
          targets = targets.to(preds.device)
          class_weights = torch.ones(targets.shape, device=preds.device)
  
          if args.dataset_type == 'multiclass':
              targets = targets.long()
              loss = torch.cat([loss_func(preds[:, target_index, :], targets[:, target_index]).unsqueeze(1) for target_index in range(preds.size(1))], dim=1) * class_weights * mask
          else:
              loss = loss_func(preds, targets) * class_weights * mask
          loss = loss.sum() / mask.sum()
  
          loss_sum += loss.item()
          iter_count += len(batch)
  
          loss.backward()
          if args.grad_clip:
              nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
          optimizer.step()
  
          if isinstance(scheduler, NoamLR):
              scheduler.step()
  
          n_iter += len(batch)
  
          # Log and/or add to tensorboard
          if (n_iter // args.batch_size) % args.log_frequency == 0:
              lrs = scheduler.get_lr()
              pnorm = compute_pnorm(model)
              gnorm = compute_gnorm(model)
              loss_avg = loss_sum / iter_count
              loss_sum, iter_count = 0, 0
  
              lrs_str = ', '.join(f'lr_{i} = {lr:.4e}' for i, lr in enumerate(lrs))
              debug(f'Loss = {loss_avg:.4e}, PNorm = {pnorm:.4f}, GNorm = {gnorm:.4f}, {lrs_str}')
  
              if writer is not None:
                  writer.add_scalar('train_loss', loss_avg, n_iter)
                  writer.add_scalar('param_norm', pnorm, n_iter)
                  writer.add_scalar('gradient_norm', gnorm, n_iter)
                  for i, lr in enumerate(lrs):
                      writer.add_scalar(f'learning_rate_{i}', lr, n_iter)
  
      return n_iter
  