code: def __init__(
      self,
      model: LanguageModel,
      corpus: TextCorpus,
      optimizer: Type[Optimizer] = SGD,
      test_mode: bool = False,
      epoch: int = 0,
      split: int = 0,
      loss: float = 10000,
      optimizer_state: Optional[Dict[str, Any]] = None,
      scaler_state: Optional[Dict[str, Any]] = None,
  ) -> None:
      self.model: LanguageModel = model
      self.optimizer: Type[Optimizer] = optimizer
      self.corpus: TextCorpus = corpus
      self.test_mode: bool = test_mode
  
      self.loss_function = torch.nn.CrossEntropyLoss()
      self.log_interval = 100
      self.epoch = epoch
      self.split = split
      self.loss = loss
      self.optimizer_state = optimizer_state
      self.scaler_state = scaler_state
  
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 1 to create a new LanguageModelTrainer object  with example: obj = LanguageModelTrainer(model, corpus, optimizer, test_mode, epoch, split, loss, optimizer_state, scaler_state)