code: def train(
      self,
      base_path: Union[Path, str],
      sequence_length: int,
      learning_rate: float = 20,
      mini_batch_size: int = 100,
      anneal_factor: float = 0.25,
      patience: int = 10,
      clip=0.25,
      max_epochs: int = 1000,
      checkpoint: bool = False,
      grow_to_sequence_length: int = 0,
      num_workers: int = 2,
      use_amp: bool = False,
      **kwargs,
  ):
      # cast string to Path
      base_path = Path(base_path)
  
      number_of_splits: int = len(self.corpus.train)
  
      val_data = self._batchify(self.corpus.valid, mini_batch_size)
  
      # error message if the validation dataset is too small
      if val_data.size(0) == 1:
          raise RuntimeError(
              f"ERROR: Your validation dataset is too small. For your "
              f"mini_batch_size, the data needs to "
              f"consist of at least {mini_batch_size * 2} characters!"
          )
  
      base_path.mkdir(parents=True, exist_ok=True)
      loss_txt = base_path / "loss.txt"
      savefile = base_path / "best-lm.pt"
  
      try:
          log_handler = add_file_handler(
              log, base_path / "training.log"
          )
  
          best_val_loss = self.loss
          kwargs["lr"] = learning_rate
          optimizer = self.optimizer(self.model.parameters(), **kwargs)
  
          scaler = torch.cuda.amp.GradScaler(
              enabled=use_amp and flair.device.type != "cpu"
          )
  
          if self.scaler_state:
              scaler.load_state_dict(self.scaler_state)
  
          if self.optimizer_state is not None:
              optimizer.load_state_dict(self.optimizer_state)
  
          if isinstance(optimizer, (AdamW, SGDW)):
              scheduler: ReduceLROnPlateau = ReduceLRWDOnPlateau(
                  optimizer,
                  verbose=True,
                  factor=anneal_factor,
                  patience=patience,
              )
          else:
              scheduler = ReduceLROnPlateau(
                  optimizer,
                  verbose=True,
                  factor=anneal_factor,
                  patience=patience,
              )
  
          training_generator = DataLoader(
              self.corpus.train, shuffle=False, num_workers=num_workers
          )
  
          for epoch in range(self.epoch, max_epochs):
              epoch_start_time = time.time()
              # Shuffle training files randomly after serially iterating
              # through corpus one
              if epoch > 0:
                  training_generator = DataLoader(
                      self.corpus.train,
                      shuffle=True,
                      num_workers=num_workers,
                  )
                  self.model.save_checkpoint(
                      base_path / f"epoch_{epoch}.pt",
                      optimizer,
                      epoch,
                      0,
                      best_val_loss,
                  )
  
              # iterate through training data, starting at
              # self.split (for checkpointing)
              for curr_split, train_slice in enumerate(
                  training_generator, self.split
              ):
                  if sequence_length < grow_to_sequence_length:
                      sequence_length += 1
                  log.info(f"Sequence length is {sequence_length}")
  
                  split_start_time = time.time()
                  # off by one for printing
                  curr_split += 1
                  train_data = self._batchify(
                      train_slice.flatten(), mini_batch_size
                  )
  
                  log.info(
                      "Split %d" % curr_split
                      + f"\t - ({datetime.datetime.now():%H:%M:%S})"
                  )
  
                  for group in optimizer.param_groups:
                      learning_rate = group["lr"]
  
                  # go into train mode
                  self.model.train()
  
                  # reset variables
                  hidden = self.model.init_hidden(mini_batch_size)
  
                  # not really sure what this does
                  ntokens = len(self.corpus.dictionary)
  
                  total_loss = torch.zeros(1, device=flair.device)
                  start_time = time.time()
  
                  for batch, i in enumerate(
                      range(0, train_data.size(0) - 1, sequence_length)
                  ):
                      data, targets = self._get_batch(
                          train_data, i, sequence_length
                      )
  
                      if not data.is_cuda and cuda.is_available():
                          log.info(
                              "Batch %d is not on CUDA, training will be very slow"
                              % (batch)
                          )
                          raise Exception("data isnt on cuda")
  
                      self.model.zero_grad()
                      optimizer.zero_grad()
                      with torch.autocast(
                          device_type=flair.device.type, enabled=use_amp
                      ):
                          # do the forward pass in the model
                          (
                              output,
                              rnn_output,
                              hidden,
                          ) = self.model.forward(data, hidden)
  
                          # try to predict the targets
                          loss = self.loss_function(
                              output.view(-1, ntokens), targets
                          )
                      # Backward
                      scaler.scale(loss).backward()
  
                      scaler.unscale_(optimizer)
  
                      # `clip_grad_norm` helps prevent the exploding gradient
                      # problem in RNNs / LSTMs.
                      torch.nn.utils.clip_grad_norm_(
                          self.model.parameters(), clip
                      )
                      scaler.step(optimizer)
                      scaler.update()
  
                      total_loss += loss.data
  
                      # We detach the hidden state from how it was
                      # previously produced.
                      # If we didn't, the model would try backpropagating
                      # all the way to start of the dataset.
                      hidden = self._repackage_hidden(hidden)
  
                      # explicitly remove loss to clear up memory
                      del loss, output, rnn_output
  
                      if batch % self.log_interval == 0 and batch > 0:
                          cur_loss = (
                              total_loss.item() / self.log_interval
                          )
                          elapsed = time.time() - start_time
                          log.info(
                              f"| split {curr_split:3d}/{number_of_splits:3d} | {batch:5d}/{len(train_data) // sequence_length:5d} batches "
                              f"| ms/batch {elapsed * 1000 / self.log_interval:5.2f} | loss {cur_loss:5.4f} | ppl {math.exp(cur_loss):5.4f}"
                          )
                          total_loss = torch.zeros(
                              1, device=flair.device
                          )
                          start_time = time.time()
  
                  ##########################################################
                  self.model.eval()
  
                  val_loss = self.evaluate(
                      val_data, mini_batch_size, sequence_length
                  )
  
                  # Save the model if the validation loss is the best we've
                  # seen so far.
                  if val_loss < best_val_loss:
                      self.model.save(savefile)
                      best_val_loss = val_loss
                      log.info("best split so far")
  
                  scheduler.step(val_loss)
  
                  log.info(f"best loss so far {best_val_loss:5.8f}")
  
                  log.info(self.model.generate_text())
  
                  if checkpoint:
                      self.model.save_checkpoint(
                          base_path / "checkpoint.pt",
                          optimizer,
                          epoch,
                          curr_split,
                          best_val_loss,
                      )
  
                  ##########################################################
                  # print info
                  ##########################################################
                  log.info("-" * 89)
  
                  summary = (
                      f"| end of split {curr_split:3d} /{number_of_splits:3d} | epoch {epoch + 1:3d} | time: "
                      f"{(time.time() - split_start_time):5.2f}s | valid loss {val_loss:5.4f} | valid ppl "
                      f"{math.exp(val_loss):5.4f} | learning rate {learning_rate:3.4f}"
                  )
  
                  with open(loss_txt, "a") as myfile:
                      myfile.write("%s\n" % summary)
  
                  log.info(summary)
                  log.info("-" * 89)
                  log.info(
                      "%d seconds for train split %d"
                      % (time.time() - split_start_time, curr_split)
                  )
  
              log.info(
                  "Epoch time: %.2f" % (time.time() - epoch_start_time)
              )
  
      except KeyboardInterrupt:
          log.info("-" * 89)
          log.info("Exiting from training early")
      finally:
          if log_handler is not None:
              log_handler.close()
              log.removeHandler(log_handler)
  
      ###############################################################################
      # final testing
      ###############################################################################
      test_data = self._batchify(self.corpus.test, mini_batch_size)
      test_loss = self.evaluate(
          test_data, mini_batch_size, sequence_length
      )
  
      summary = f"TEST: valid loss {test_loss:5.4f} | valid ppl {math.exp(test_loss):8.4f}"
      with open(loss_txt, "a") as myfile:
          myfile.write("%s\n" % summary)
  
      log.info(summary)
      log.info("-" * 89)
  
imports: import datetime
  import logging
  import math
  import random
  import time
  from pathlib import Path
  from typing import Any, Dict, Iterable, Optional, Type, Union
  import torch
  from torch import cuda
  from torch.optim import AdamW, Optimizer
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  from torch.optim.sgd import SGD
  from torch.utils.data import DataLoader, Dataset
  import flair
  from flair.data import Dictionary
  from flair.models import LanguageModel
  from flair.optim import SGDW, ReduceLRWDOnPlateau
  from flair.training_utils import add_file_handler
label_desc: Use image node_id 2 for calling the LanguageModelTrainer obj's underlying member method code with example usage: obj.train(base_path, sequence_length, learning_rate, mini_batch_size, anneal_factor, patience, clip, max_epochs, checkpoint, grow_to_sequence_length, num_workers, use_amp) without return types