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(
          "--distributed-backend",
          default="nccl",
          choices=["nccl", "gloo"],
          help="Which backend to use for distributed training.",
      )
      group.add_argument(
          "--DDP-impl",
          default="local",
          choices=["local", "torch"],
          help="which DistributedDataParallel implementation "
          "to use.",
      )
      group.add_argument(
          "--no-contiguous-buffers-in-local-ddp",
          action="store_false",
          help="If set, dont use " "contiguous buffer in local DDP.",
          dest="use_contiguous_buffers_in_local_ddp",
      )
      group.add_argument(
          "--no-scatter-gather-tensors-in-pipeline",
          action="store_false",
          help="Use scatter/gather to optimize communication of tensors in pipeline",
          dest="scatter_gather_tensors_in_pipeline",
      )
      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.)",
      )
      return parser
  
imports: import argparse
  import os
  import torch
label_desc: Use image node_id 13 for calling a global function with example usage: _add_distributed_args(parser) and returns: parser