code: def run_dlrm_main(
      num_trainers: int = 8, *script_args: str
  ) -> specs.AppDef:
      """
      Args:
          num_trainers: The number of trainers to use.
          script_args: A variable number of parameters to provide dlrm_main.py.
      """
      cwd = os.getcwd()
      entrypoint = os.path.join(cwd, "train_torchrec.py")
  
      user = os.environ.get("USER")
      image = f"/data/home/{user}"
  
      if num_trainers > 8 and num_trainers % 8 != 0:
          raise ValueError(
              "Trainer jobs spanning multiple hosts must be in multiples of 8."
          )
      nproc_per_node = 8 if num_trainers >= 8 else num_trainers
      num_replicas = max(num_trainers // 8, 1)
  
      return ddp(
          *script_args,
          name="train_dlrm",
          image=image,
          # AWS p4d instance (https://aws.amazon.com/ec2/instance-types/p4/).
          cpu=96,
          gpu=8,
          memMB=-1,
          script=entrypoint,
          j=f"{num_replicas}x{nproc_per_node}",
      )
  
imports: import os
  import torchx.specs
  from torchx.components.dist import ddp
label_desc: Use image node_id 1 for calling a global function with example usage: run_dlrm_main(num_trainers) and returns: ddp