**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
if start_date and not isinstance(start_date, datetime):
            self.log.warning("start_date for %s isn't datetime.datetime", self)
        elif start_date:
            self.start_date = timezone.convert_to_utc(start_date)
        self.end_date = end_date
        if end_date:
            self.end_date = timezone.convert_to_utc(end_date)
        if not TriggerRule.is_valid(trigger_rule):
            raise AirflowException(
                "The trigger_rule must be one of {all_triggers},"
                "'{d}.{t}'; received '{tr}'.".format(
                    all_triggers=TriggerRule.all_triggers(),
                    d=dag.dag_id if dag else "",
                    t=task_id,
                    tr=trigger_rule,
                )
            )
        self.trigger_rule = trigger_rule
        self.depends_on_past = depends_on_past
        self.wait_for_downstream = wait_for_downstream
        if wait_for_downstream:
            self.depends_on_past = True
        if retries is not None and not isinstance(retries, int):
            try:
                parsed_retries = int(retries)
            except (TypeError, ValueError):
                raise AirflowException(f"'retries' type must be int, not {type(retries).__name__}")
            self.log.warning("Implicitly converting 'retries' for %s from %r to int", self, retries)
            retries = parsed_retries
        self.retries = retries