def wrap_udf_in_torch_dist_launcher(
    udf_command: str,
    num_trainers: int,
    num_nodes: int,
    node_rank: int,
    master_addr: str,
    master_port: int,
) -> str:
    """Wraps the user-defined function (udf_command) with the torch.distributed.launch module.

     Example: if udf_command is "python3 run/some/trainer.py arg1 arg2", then new_df_command becomes:
         "python3 -m torch.distributed.launch <TORCH DIST ARGS> run/some/trainer.py arg1 arg2

    udf_command is assumed to consist of pre-commands (optional) followed by the python launcher script (required):
    Examples:
        # simple
        python3.7 path/to/some/trainer.py arg1 arg2

        # multi-commands
        (cd some/dir && python3.7 path/to/some/trainer.py arg1 arg2)

    IMPORTANT: If udf_command consists of multiple python commands, then this will result in undefined behavior.

    Args:
        udf_command:
        num_trainers:
        num_nodes:
        node_rank:
        master_addr:
        master_port:

    Returns:

    """
    torch_dist_cmd = construct_torch_dist_launcher_cmd(
        num_trainers=num_trainers,
        num_nodes=num_nodes,
        node_rank=node_rank,
        master_addr=master_addr,
        master_port=master_port,
    )
    # Auto-detect the python binary that kicks off the distributed trainer code.
    # Note: This allowlist order matters, this will match with the FIRST matching entry. Thus, please add names to this
    #       from most-specific to least-specific order eg:
    #           (python3.7, python3.8) -> (python3)
    # The allowed python versions are from this: https://www.dgl.ai/pages/start.html
    python_bin_allowlist = (
        "python3.6",
        "python3.7",
        "python3.8",
        "python3.9",
        "python3",
        # for backwards compatibility, accept python2 but technically DGL is a py3 library, so this is not recommended
        "python2.7",
        "python2",
    )
    # If none of the candidate python bins match, then we go with the default `python`
    python_bin = "python"
    for candidate_python_bin in python_bin_allowlist:
        if candidate_python_bin in udf_command:
            python_bin = candidate_python_bin
            break

    # transforms the udf_command from:
    #     python path/to/dist_trainer.py arg0 arg1
    # to:
    #     python -m torch.distributed.launch [DIST TORCH ARGS] path/to/dist_trainer.py arg0 arg1
    # Note: if there are multiple python commands in `udf_command`, this may do the Wrong Thing, eg launch each
    #       python command within the torch distributed launcher.
    new_udf_command = udf_command.replace(
        python_bin, f"{python_bin} {torch_dist_cmd}"
    )

    return new_udf_command
Wraps the user-defined function (udf_command) with the torch.distributed.launch module.

     Example: if udf_command is "python3 run/some/trainer.py arg1 arg2", then new_df_command becomes:
         "python3 -m torch.distributed.launch <TORCH DIST ARGS> run/some/trainer.py arg1 arg2

    udf_command is assumed to consist of pre-commands (optional) followed by the python launcher script (required):
    Examples:
        # simple
        python3.7 path/to/some/trainer.py arg1 arg2

        # multi-commands
        (cd some/dir && python3.7 path/to/some/trainer.py arg1 arg2)

    IMPORTANT: If udf_command consists of multiple python commands, then this will result in undefined behavior.

    Args:
        udf_command:
        num_trainers:
        num_nodes:
        node_rank:
        master_addr:
        master_port:

    Returns:

    
tools/distgraphlaunch.py