code: def __init__(
      self,
      gpus=None,
      gpu_memory_limit=None,
      allow_parallel_threads=True,
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      # Only return results from rank 0 to reduce network overhead
      self.train = self.distributed.return_first(self.train)
      self.train_online = self.distributed.return_first(
          self.train_online
      )
  
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 1 to create a new RemoteLLMTrainer object from inherited base classes: NoneTrainer with example: obj = RemoteLLMTrainer(gpus, gpu_memory_limit, allow_parallel_threads)