code: def _add_distributed_args(parser):
      group = parser.add_argument_group(title="distributed")
  
      group.add_argument(
          "--tensor-model-parallel-size",
          type=int,
          default=1,
          help="Degree of tensor model parallelism.",
      )
      group.add_argument(
          "--pipeline-model-parallel-size",
          type=int,
          default=1,
          help="Degree of pipeline model parallelism.",
      )
      group.add_argument(
          "--pipeline-model-parallel-split-rank",
          type=int,
          default=None,
          help="Rank where encoder and decoder should be split.",
      )
      group.add_argument(
          "--model-parallel-size",
          type=int,
          default=None,
          help="Old model parallel argument, do not use. Use "
          "--tensor-model-parallel-size instead.",
      )
      group.add_argument(
          "--num-layers-per-virtual-pipeline-stage",
          type=int,
          default=None,
          help="Number of layers per virtual pipeline stage",
      )
      group.add_argument(
          "--no-overlap-p2p-communication",
          action="store_false",
          help="overlap pipeline parallel communication with forward and backward chunks",
          dest="overlap_p2p_comm",
      )
      group.add_argument(
          "--distributed-backend",
          default="nccl",
          choices=["nccl", "gloo"],
          help="Which backend to use for distributed training.",
      )
      group.add_argument(
          "--distributed-timeout-minutes",
          type=int,
          default=10,
          help="Timeout minutes for torch.distributed.",
      )
      group.add_argument(
          "--overlap-grad-reduce",
          action="store_true",
          default=False,
          help="If set, overlap DDP grad reduce.",
      )
      group.add_argument(
          "--no-delay-grad-reduce",
          action="store_false",
          help="If not set, delay / synchronize grad reductions in all but first PP stage.",
          dest="delay_grad_reduce",
      )
      group.add_argument(
          "--overlap-param-gather",
          action="store_true",
          default=False,
          help="If set, overlap param all-gather in distributed optimizer.",
      )
      group.add_argument(
          "--delay-param-gather",
          action="store_true",
          default=False,
          help="If set, delay / synchronize param all-gathers in all but first PP stage.",
      )
      group.add_argument(
          "--no-scatter-gather-tensors-in-pipeline",
          action="store_false",
          help="If not set, use scatter/gather to optimize communication of tensors in pipeline.",
          dest="scatter_gather_tensors_in_pipeline",
      )
      group.add_argument(
          "--use-ring-exchange-p2p",
          action="store_true",
          default=False,
          help="If set, use custom-built ring exchange "
          "for p2p communications. Note that this option will require "
          "a custom built image that support ring-exchange p2p.",
      )
      group.add_argument(
          "--local_rank",
          type=int,
          default=None,
          help="local rank passed from distributed launcher.",
      )
      group.add_argument(
          "--lazy-mpu-init",
          type=bool,
          required=False,
          help="If set to True, initialize_megatron() "
          "skips DDP initialization and returns function to "
          "complete it instead.Also turns on "
          "--use-cpu-initialization flag. This is for "
          "external DDP manager.",
      )
      group.add_argument(
          "--use-cpu-initialization",
          action="store_true",
          default=None,
          help="If set, affine parallel weights "
          "initialization uses CPU",
      )
      group.add_argument(
          "--empty-unused-memory-level",
          default=0,
          type=int,
          choices=[0, 1, 2],
          help="Call torch.cuda.empty_cache() each iteration "
          "(training and eval), to reduce fragmentation."
          "0=off, 1=moderate, 2=aggressive.",
      )
      group.add_argument(
          "--standalone-embedding-stage",
          action="store_true",
          default=False,
          help="If set, *input* embedding layer "
          "is placed on its own pipeline stage, without any "
          "transformer layers. (For T5, this flag currently only "
          "affects the encoder embedding.)",
      )
      group.add_argument(
          "--use-distributed-optimizer",
          action="store_true",
          help="Use distributed optimizer.",
      )
      group.add_argument(
          "--expert-model-parallel-size",
          type=int,
          default=1,
          help="Degree of expert model parallelism.",
      )
      group.add_argument(
          "--context-parallel-size",
          type=int,
          default=1,
          help="Degree of context parallelism.",
      )
      group.add_argument(
          "--nccl-communicator-config-path",
          type=str,
          default=None,
          help="Path to the yaml file with NCCL communicator "
          "configurations. The number of min/max thread groups and thread "
          "group cluster size of each communicator can be configured by "
          "setting `min_ctas`, `max_ctas`, and `cga_cluster_size`.",
      )
      return parser
  
imports: import argparse
  import dataclasses
  import json
  import os
  import torch
  import types
  import torch.nn.functional
  from megatron.global_vars import set_retro_args, get_retro_args
  from tools.retro.utils import get_args_path
  from megatron.core.models.retro import RetroConfig
  from megatron.core.transformer import TransformerConfig
label_desc: Use image node_id 17 for calling a global function with example usage: _add_distributed_args(parser) and returns: parser