**Description**
The `airflow.models.baseoperator.chain()` is a very useful and convenient way to add sequential task dependencies in DAGs.  This function has [recently been updated](https://github.com/apache/airflow/issues/16635) to support `BaseOperator` and `XComArgs` but should also be able to support `Labels` as well.

**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 with a `Label`.  This use case can occur when attempting to chain multiple branch labels and the next sequential task.

With the new update (yet to be released), users will receive the following exception when attempting to chain an `XComArg` and `Label`:

```bash
TypeError: Chain not supported between instances of <class 'airflow.utils.edgemodifier.EdgeModifier'> and <class 'airflow.models.xcom_arg.XComArg'>
```


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

**Related Issues**
None

airflow/models/baseoperator.py
self.priority_weight = priority_weight
        if not WeightRule.is_valid(weight_rule):
            raise AirflowException(
                "The weight_rule must be one of {all_weight_rules},"
                "'{d}.{t}'; received '{tr}'.".format(
                    all_weight_rules=WeightRule.all_weight_rules,
                    d=dag.dag_id if dag else "",
                    t=task_id,
                    tr=weight_rule,
                )
            )
        self.weight_rule = weight_rule
        self.resources: Optional[Resources] = Resources(**resources) if resources else None
        self.run_as_user = run_as_user
        self.task_concurrency = task_concurrency
        self.executor_config = executor_config or {}
        self.do_xcom_push = do_xcom_push
        self.doc_md = doc_md
        self.doc_json = doc_json
        self.doc_yaml = doc_yaml
        self.doc_rst = doc_rst
        self.doc = doc
        
        self._upstream_task_ids: Set[str] = set()
        self._downstream_task_ids: Set[str] = set()
        self._dag = None
        self.dag = dag or DagContext.get_current_dag()
        
        
        from airflow.models.dag import DAG