code: def _initialize_tp_communicators():
      """initializing the communicators with user buffers for high-performance tensor-model-parallel
      communication overlap"""
  
      try:
          import yaml
  
          import transformer_engine
          from transformer_engine.pytorch import module as te_module
  
      except ImportError:
          raise RuntimeError(
              "Tensor Parallel Communication/GEMM Overlap optimization needs 'yaml' and "
              "'transformer_engine' packages"
          )
  
      args = get_args()
  
      if args.tp_comm_overlap_cfg is not None:
          with open(args.tp_comm_overlap_cfg, "r") as stream:
              ub_cfgs = yaml.safe_load(stream)
      else:
          ub_cfgs = {}
  
      input_shape = [
          args.seq_length * args.micro_batch_size,
          args.hidden_size,
      ]
  
      # We create a MPI process group, which is needed to bootstrap the pipelined
      # tensor-model-parallel communication overlap
      torch.distributed.new_group(backend="mpi")
  
      te_module.base.initialize_ub(
          shape=input_shape,
          tp_size=args.tensor_model_parallel_size,
          use_fp8=(args.fp8 is not None),
          ub_cfgs=ub_cfgs,
      )
  
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 3 for calling a global function with example usage: _initialize_tp_communicators() without return types