code: def initialize_megatron(
      extra_args_provider=None,
      args_defaults={},
      ignore_unknown_args=False,
      allow_no_cuda=False,
      skip_mpu_initialization=False,
  ):
      """Set global variables, initialize distributed, and
      set autoresume and random seeds.
      `allow_no_cuda` should not be set unless using megatron for cpu only
      data processing. In general this arg should not be set unless you know
      what you are doing.
      Returns a function to finalize distributed env initialization
      (optionally, only when args.lazy_mpu_init == True)
      """
      if not allow_no_cuda:
          # Make sure cuda is available.
          assert torch.cuda.is_available(), "Megatron requires CUDA."
  
      # Parse arguments
      args = parse_args(extra_args_provider, ignore_unknown_args)
  
      if args.use_checkpoint_args or args_defaults.get(
          "use_checkpoint_args", False
      ):
          assert (
              args.load is not None
          ), "--use-checkpoints-args requires --load argument"
          load_args_from_checkpoint(args)
  
      validate_args(args, args_defaults)
  
      # set global args, build tokenizer, and set adlr-autoresume,
      # tensorboard-writer, and timers.
      set_global_variables(args)
  
      # torch.distributed initialization
      def finish_mpu_init():
          args = get_args()
          # Pytorch distributed.
          _initialize_distributed()
  
          # Random seeds for reproducibility.
          if args.rank == 0:
              print(
                  "> setting random seeds to {} ...".format(args.seed)
              )
          _set_random_seed(args.seed, args.data_parallel_random_init)
  
      if skip_mpu_initialization:
          return None
  
      args = get_args()
      if args.lazy_mpu_init:
          # TODO is this still a necessary option?
          args.use_cpu_initialization = True
          # delayed initialization of DDP-related stuff
          # We only set basic DDP globals
          mpu.set_tensor_model_parallel_world_size(
              args.tensor_model_parallel_size
          )
          # and return function for external DDP manager
          # to call when it has DDP initialized
          mpu.set_tensor_model_parallel_rank(args.rank)
          return finish_mpu_init
      else:
          # Megatron's MPU is the master. Complete initialization right away.
          finish_mpu_init()
  
          # Autoresume.
          _init_autoresume()
  
          # Compile dependencies.
          _compile_dependencies()
  
          if args.tp_comm_overlap:
              _initialize_tp_communicators()
  
          # No continuation function
          return None
  
imports: import random
  import os
  import time
  import numpy
  import torch
  from datetime import timedelta
  from megatron import fused_kernels
  from megatron import get_adlr_autoresume
  from megatron import get_args
  from megatron import get_tensorboard_writer
  from megatron.core import mpu, tensor_parallel
  from megatron.arguments import parse_args, validate_args
  from megatron.checkpointing import load_args_from_checkpoint
  from megatron.global_vars import set_global_variables
  from megatron.model.transformer import bias_dropout_add_fused_train
  from megatron.model.fused_bias_gelu import bias_gelu
label_desc: Use image node_id 1 for calling a global function with example usage: initialize_megatron(extra_args_provider, args_defaults, ignore_unknown_args, allow_no_cuda, skip_mpu_initialization) and returns: None, finish_mpu_init, None