code: def train(
      self,
      base_path,
      anneal_factor: float = 0.5,
      patience: int = 3,
      min_learning_rate: Union[float, List[float]] = 0.0001,
      initial_extra_patience: int = 0,
      anneal_with_restarts: bool = False,
      learning_rate: float = 0.1,
      decoder_learning_rate: Optional[float] = None,
      mini_batch_size: int = 32,
      eval_batch_size: int = 64,
      mini_batch_chunk_size: Optional[int] = None,
      max_epochs: int = 100,
      optimizer: Type[torch.optim.Optimizer] = torch.optim.SGD,
      train_with_dev: bool = False,
      train_with_test: bool = False,
      reduce_transformer_vocab: bool = False,
      # evaluation and monitoring
      main_evaluation_metric: Tuple[str, str] = (
          "micro avg",
          "f1-score",
      ),
      monitor_test: bool = False,
      monitor_train_sample: float = 0.0,
      use_final_model_for_eval: bool = False,
      gold_label_dictionary_for_eval: Optional[Dictionary] = None,
      exclude_labels: List[str] = [],
      # sampling and shuffling
      sampler=None,
      shuffle: bool = True,
      shuffle_first_epoch: bool = True,
      # evaluation and monitoring
      embeddings_storage_mode: str = "cpu",
      epoch: int = 0,
      # when and what to save
      save_final_model: bool = True,
      save_optimizer_state: bool = False,
      save_model_each_k_epochs: int = 0,
      # logging parameters
      create_file_logs: bool = True,
      create_loss_file: bool = True,
      write_weights: bool = False,
      # plugins
      plugins: Optional[List[TrainerPlugin]] = None,
      attach_default_scheduler: bool = True,
      **kwargs,
  ):
      if plugins is None:
          plugins = []
  
      if attach_default_scheduler:
          # activate annealing plugin
          plugins.append(
              AnnealingPlugin(
                  base_path=base_path,
                  anneal_factor=anneal_factor,
                  patience=patience,
                  min_learning_rate=min_learning_rate,
                  initial_extra_patience=initial_extra_patience,
                  anneal_with_restarts=anneal_with_restarts,
              )
          )
  
      # call self.train_custom with all parameters (minus the ones specific to the AnnealingPlugin)
      local_variables = locals()
      for var in [
          "self",
          "anneal_factor",
          "patience",
          "min_learning_rate",
          "initial_extra_patience",
          "anneal_with_restarts",
          "attach_default_scheduler",
          "kwargs",
      ]:
          local_variables.pop(var)
      return self.train_custom(**local_variables, **kwargs)
  
imports: import contextlib
  import inspect
  import logging
  import os
  import random
  import time
  import warnings
  from inspect import signature
  from pathlib import Path
  from typing import List, Optional, Tuple, Type, Union
  import torch
  from torch.optim.sgd import SGD
  from torch.utils.data.dataset import ConcatDataset
  import flair
  import flair.nn
  from flair.data import Corpus, Dictionary, _len_dataset
  from flair.datasets import DataLoader
  from flair.samplers import FlairSampler
  from flair.trainers.plugins import AnnealingPlugin, CheckpointPlugin, LinearSchedulerPlugin, LogFilePlugin, LossFilePlugin, MetricName, MetricRecord, Pluggable, ReduceTransformerVocabPlugin, TrainerPlugin, TrainingInterrupt, WeightExtractorPlugin
  from flair.training_utils import identify_dynamic_embeddings, log_line, store_embeddings
label_desc: Use image node_id 7 for calling the ModelTrainer obj's underlying member method code with example usage: obj.train(base_path, anneal_factor, patience, min_learning_rate, initial_extra_patience, anneal_with_restarts, learning_rate, decoder_learning_rate, mini_batch_size, eval_batch_size, mini_batch_chunk_size, max_epochs, optimizer, train_with_dev, train_with_test, reduce_transformer_vocab, main_evaluation_metric, monitor_test, monitor_train_sample, use_final_model_for_eval, gold_label_dictionary_for_eval, exclude_labels, sampler, shuffle, shuffle_first_epoch, embeddings_storage_mode, epoch, save_final_model, save_optimizer_state, save_model_each_k_epochs, create_file_logs, create_loss_file, write_weights, plugins, attach_default_scheduler) and returns: self