code: def __init__(self, context: DeepSpeedTrialContext) -> None:
      self.context = context
      self.exp_config = self.context.get_experiment_config()
      self.args = AttrMap(self.context.get_hparams())
  
      # Initalize and get arguments, timers, and Tensorboard writer.
      try:
          self.neox_args = get_neox_args(self.context)
      except:
          traceback.print_exc()
          raise InvalidHP("Could not parse neox_args.")
      logging.info(self.neox_args)
      self.writer = self.context.get_tensorboard_writer()
      self.neox_args.tensorboard_writer = self.writer
      self.neox_args.configure_distributed_args()
      # The tokenizer needs to be built before model initialization in order to set the
      # required padded_vocab_size argument.
      self.neox_args.build_tokenizer()
      megatron_train.initialize_megatron(neox_args=self.neox_args)
      self.timers = megatron_utils.Timers(
          use_wandb=False,
          tensorboard_writer=self.neox_args.tensorboard_writer,
      )
  
      # Model, optimizer, and learning rate.
      self.timers("model and optimizer").start()
      with deepspeed.zero.Init(
          enabled=self.neox_args.zero_optimization["stage"] == 3
      ):
          (
              model,
              self.optimizer,
              self.lr_scheduler,
          ) = megatron_train.setup_model_and_optimizer(
              neox_args=self.neox_args
          )
      self.model = self.context.wrap_model_engine(model)
      self.context.set_mpu(
          ModelParallelUnit(
              mpu.get_data_parallel_rank(),
              mpu.get_data_parallel_world_size(),
              should_report_metrics=True,
              should_build_data_loader=self.should_build_data_loader(),
          )
      )
      self.timers("model and optimizer").stop()
  
      # Print setup timing.
      megatron_utils.print_rank_0("done with setups ...")
      self.timers.log(["model and optimizer"])
      megatron_utils.print_rank_0("training ...")
  
      # For tracking.
      if not self.args.search_world_size:
          self.reducer = self.context.wrap_reducer(
              LMReducers(self.neox_args),
              for_training=False,
              for_validation=True,
          )
      self.report_memory_flag = True
      self.total_train_loss_dict = {}
      self.total_val_loss_dict = {}
      self.tflops = 0
      self.reported_flops = False
      self.overflow_monitor = megatron_utils.OverflowMonitor(
          self.optimizer
      )
      self.noise_scale_logger = megatron_utils.get_noise_scale_logger(
          self.neox_args
      )
      self.timers("interval time").start()
  
imports: import logging
  import pathlib
  import traceback
  from datetime import datetime
  import deepspeed
  import megatron.training
  import megatron.utils
  import torch
  from attrdict import AttrMap
  from det_utils import EarlyStoppingCallback, EvalHarness, LMReducers, TensorboardWriter, get_neox_args
  from megatron import mpu
  from megatron.checkpointing import load_checkpoint, save_checkpoint
  from megatron.data.data_utils import build_datasets_from_neox_args
  from determined import LOG_FORMAT, InvalidHP
  from determined.pytorch import DataLoader
  from determined.pytorch.deepspeed import DeepSpeedTrial, DeepSpeedTrialContext, ModelParallelUnit
label_desc: Use image node_id 1 to create a new GPT2Trial object from inherited base classes: DeepSpeedTrial with example: obj = GPT2Trial(context)