code: def __init__(
      self,
      config: FineTuneTrainerConfig,
      model: LLM,
      resume: float = False,
      skip_save_model: bool = False,
      skip_save_progress: bool = False,
      skip_save_log: bool = False,
      callbacks: List = None,
      report_tqdm_to_ray=False,
      random_seed: float = default_random_seed,
      distributed: Optional[DistributedStrategy] = None,
      device: Optional[str] = None,
      **kwargs,
  ):
      super().__init__(
          config,
          model,
          resume,
          skip_save_model,
          skip_save_progress,
          skip_save_log,
          callbacks,
          report_tqdm_to_ray,
          random_seed,
          distributed,
          device,
          **kwargs,
      )
  
imports: import logging
  import os
  import time
  from typing import Callable, Dict, List, Optional, Union
  from torch.utils.tensorboard import SummaryWriter
  from ludwig.constants import MINIMUM_BATCH_SIZE, TEST, TRAINING, VALIDATION
  from ludwig.data.dataset.base import Dataset
  from ludwig.distributed.base import DistributedStrategy, LocalStrategy
  from ludwig.features.feature_utils import LudwigFeatureDict
  from ludwig.models.llm import LLM
  from ludwig.models.predictor import LlmFineTunePredictor, LlmPredictor
  from ludwig.modules.metric_modules import get_initial_validation_value
  from ludwig.schema.trainer import BaseTrainerConfig, FineTuneTrainerConfig, NoneTrainerConfig
  from ludwig.trainers.base import BaseTrainer
  from ludwig.trainers.registry import register_llm_ray_trainer, register_llm_trainer
  from ludwig.trainers.trainer import Trainer
  from ludwig.types import ModelConfigDict
  from ludwig.utils import time_utils
  from ludwig.utils.defaults import default_random_seed
  from ludwig.utils.metric_utils import TrainerMetric
  from ludwig.utils.metrics_printed_table import print_metrics_table
  from ludwig.utils.misc_utils import set_random_seed
  from ludwig.utils.torch_utils import get_torch_device
  from ludwig.utils.trainer_utils import append_metrics, get_new_progress_tracker, ProgressTracker
label_desc: Use image node_id 2 to create a new FineTuneTrainer object from inherited base classes: Trainer with example: obj = FineTuneTrainer(config, model, resume, skip_save_model, skip_save_progress, skip_save_log, callbacks, report_tqdm_to_ray, random_seed, distributed, device)