**Description**
The `airflow.models.baseoperator.chain()` is a very useful and convenient way to add sequential task dependencies in DAGs but the function only supports tasks of a `BaseOperator` type.

**Use case / motivation**
Users who create tasks via the `@task` decorator will not be able to use the `chain()` function to apply sequential dependencies that do not share an `XComArg` implicit dependency.

**Are you willing to submit a PR?**
Absolutely. 🚀 

**Related Issues**
None

airflow/models/baseoperator.py
'wait_for_downstream',
        'priority_weight',
        'sla',
        'execution_timeout',
        'on_execute_callback',
        'on_failure_callback',
        'on_success_callback',
        'on_retry_callback',
        'do_xcom_push',
    }
    
    supports_lineage = False
    
    __instantiated = False
    
    _lock_for_execution = False
    def __init__(
        self,
        task_id: str,
        owner: str = conf.get('operators', 'DEFAULT_OWNER'),
        email: Optional[Union[str, Iterable[str]]] = None,
        email_on_retry: bool = conf.getboolean('email', 'default_email_on_retry', fallback=True),
        email_on_failure: bool = conf.getboolean('email', 'default_email_on_failure', fallback=True),
        retries: Optional[int] = conf.getint('core', 'default_task_retries', fallback=0),
        retry_delay: timedelta = timedelta(seconds=300),
        retry_exponential_backoff: bool = False,
        max_retry_delay: Optional[timedelta] = None,
        start_date: Optional[datetime] = None,
        end_date: Optional[datetime] = None,
        depends_on_past: bool = False,