def get_accelerate_command(num_gpus, gradient_accumulation_steps=1, distributed_backend=None):
    """
    Generates the appropriate command to launch a training job using the `accelerate` library based on the number of GPUs
    and the specified distributed backend.

    Args:
        num_gpus (int): The number of GPUs available for training. If 0, training will be forced on CPU.
        gradient_accumulation_steps (int, optional): The number of gradient accumulation steps. Defaults to 1.
        distributed_backend (str, optional): The distributed backend to use. Can be "ddp" (Distributed Data Parallel),
                                             "deepspeed", or None. Defaults to None.

    Returns:
        list or str: The command to be executed as a list of strings. If no GPU is found, returns a CPU command string.
                     If a single GPU is found, returns a single GPU command string. Otherwise, returns a list of
                     command arguments for multi-GPU or DeepSpeed training.

    Raises:
        ValueError: If an unsupported distributed backend is specified.
    """
    if num_gpus == 0:
        logger.warning("No GPU found. Forcing training on CPU. This will be super slow!")
        return CPU_COMMAND

    if num_gpus == 1:
        return SINGLE_GPU_COMMAND

    if distributed_backend in ("ddp", None):
        return [
            "accelerate",
            "launch",
            "--multi_gpu",
            "--num_machines",
            "1",
            "--num_processes",
            str(num_gpus),
        ]
    elif distributed_backend == "deepspeed":
        return [
            "accelerate",
            "launch",
            "--use_deepspeed",
            "--zero_stage",
            "3",
            "--offload_optimizer_device",
            "none",
            "--offload_param_device",
            "none",
            "--zero3_save_16bit_model",
            "true",
            "--zero3_init_flag",
            "true",
            "--deepspeed_multinode_launcher",
            "standard",
            "--gradient_accumulation_steps",
            str(gradient_accumulation_steps),
        ]
    else:
        raise ValueError("Unsupported distributed backend")

    Generates the appropriate command to launch a training job using the `accelerate` library based on the number of GPUs
    and the specified distributed backend.

    Args:
        num_gpus (int): The number of GPUs available for training. If 0, training will be forced on CPU.
        gradient_accumulation_steps (int, optional): The number of gradient accumulation steps. Defaults to 1.
        distributed_backend (str, optional): The distributed backend to use. Can be "ddp" (Distributed Data Parallel),
                                             "deepspeed", or None. Defaults to None.

    Returns:
        list or str: The command to be executed as a list of strings. If no GPU is found, returns a CPU command string.
                     If a single GPU is found, returns a single GPU command string. Otherwise, returns a list of
                     command arguments for multi-GPU or DeepSpeed training.

    Raises:
        ValueError: If an unsupported distributed backend is specified.
    
src/autotrain/commands.py