Hi, can you explain me the following code?
"""
@app.post("/create_project", response_class=JSONResponse)
async def handle_form(
    project_name: str = Form(...),
    task: str = Form(...),
    base_model: str = Form(...),
    hardware: str = Form(...),
    params: str = Form(...),
    autotrain_user: str = Form(...),
    column_mapping: str = Form('{"default": "value"}'),
    data_files_training: List[UploadFile] = File(...),
    data_files_valid: List[UploadFile] = File(...),
):
    """
    This function is used to handle the form submission
    """
    logger.info(f"hardware: {hardware}")
    if hardware == "Local":
        running_jobs = DB.get_running_jobs()
        logger.info(f"Running jobs: {running_jobs}")
        if running_jobs:
            for _pid in running_jobs:
                proc_status = app_utils.get_process_status(_pid)
                proc_status = proc_status.strip().lower()
                if proc_status in ("completed", "error", "zombie"):
                    logger.info(f"Killing PID: {_pid}")
                    try:
                        app_utils.kill_process_by_pid(_pid)
                    except Exception as e:
                        logger.info(f"Error while killing process: {e}")
                        logger.info(f"Process {_pid} is already completed. Skipping...")
                    DB.delete_job(_pid)

        running_jobs = DB.get_running_jobs()
        if running_jobs:
            logger.info(f"Running jobs: {running_jobs}")
            raise HTTPException(
                status_code=409, detail="Another job is already running. Please wait for it to finish."
            )

    if HF_TOKEN is None:
        return {"error": "HF_TOKEN not set"}

    params = json.loads(params)
    column_mapping = json.loads(column_mapping)

    training_files = [f.file for f in data_files_training if f.filename != ""]
    validation_files = [f.file for f in data_files_valid if f.filename != ""] if data_files_valid else []

    if task == "image-classification":
        dset = AutoTrainImageClassificationDataset(
            train_data=training_files[0],
            token=HF_TOKEN,
            project_name=project_name,
            username=autotrain_user,
            valid_data=validation_files[0] if validation_files else None,
            percent_valid=None,  # TODO: add to UI
            local=hardware.lower() == "local",
        )
    elif task == "dreambooth":
        dset = AutoTrainDreamboothDataset(
            concept_images=data_files_training,
            concept_name=params["prompt"],
            token=HF_TOKEN,
            project_name=project_name,
            username=autotrain_user,
            local=hardware.lower() == "local",
        )

    else:
        if task.startswith("llm"):
            dset_task = "lm_training"
        elif task == "text-classification":
            dset_task = "text_multi_class_classification"
        elif task == "seq2seq":
            dset_task = "seq2seq"
        elif task.startswith("tabular"):
            subtask = task.split(":")[-1].lower()
            if len(column_mapping["label"]) > 1 and subtask == "classification":
                dset_task = "tabular_multi_label_classification"
            elif len(column_mapping["label"]) == 1 and subtask == "classification":
                dset_task = "tabular_multi_class_classification"
            elif len(column_mapping["label"]) > 1 and subtask == "regression":
                dset_task = "tabular_multi_column_regression"
            elif len(column_mapping["label"]) == 1 and subtask == "regression":
                dset_task = "tabular_single_column_regression"
            else:
                raise NotImplementedError
        else:
            raise NotImplementedError
        logger.info(f"Task: {dset_task}")
        logger.info(f"Column mapping: {column_mapping}")
        dset_args = dict(
            train_data=training_files,
            task=dset_task,
            token=HF_TOKEN,
            project_name=project_name,
            username=autotrain_user,
            column_mapping=column_mapping,
            valid_data=validation_files,
            percent_valid=None,  # TODO: add to UI
            local=hardware.lower() == "local",
        )
        if task == "text-classification":
            dset_args["convert_to_class_label"] = True
        dset = AutoTrainDataset(**dset_args)
    data_path = dset.prepare()
    app_params = AppParams(
        job_params_json=json.dumps(params),
        token=HF_TOKEN,
        project_name=project_name,
        username=autotrain_user,
        task=task,
        data_path=data_path,
        base_model=base_model,
        column_mapping=column_mapping,
    )
    params = app_params.munge()
    project = AutoTrainProject(params=params, backend=hardware)
    job_id = project.create()
    monitor_url = ""
    if hardware == "Local":
        DB.add_job(job_id)
        monitor_url = "Monitor your job locally / in logs"
    elif hardware.startswith("EP"):
        monitor_url = f"https://ui.endpoints.huggingface.co/{autotrain_user}/endpoints/{job_id}"
    else:
        monitor_url = f"https://hf.co/spaces/{job_id}"
    return {"success": "true", "monitor_url": monitor_url}
"""