**Apache Airflow version**: 2.0.1


**Kubernetes version (if you are using kubernetes)** (use `kubectl version`): No

**Environment**:

- **Cloud provider or hardware configuration**: local machine
- **OS** (e.g. from /etc/os-release): 18.04.5 LTS (Bionic Beaver)
- **Kernel** (e.g. `uname -a`): wsl2

**What happened**:
Getting Pickled_id: None

```
root@12c7fd58e084:/opt/airflow# airflow tasks run example_bash_operator runme_0 now --ship-dag --interactive
[2021-05-09 13:11:33,247] {dagbag.py:487} INFO - Filling up the DagBag from /files/dags
Running <TaskInstance: example_bash_operator.runme_0 2021-05-09T13:11:31.788923+00:00 [None]> on host 12c7fd58e084
Pickled dag <DAG: example_bash_operator> as pickle_id: None
Sending to executor.
[2021-05-09 13:11:34,722] {base_executor.py:82} INFO - Adding to queue: ['airflow', 'tasks', 'run', 'example_bash_operator', 'runme_0', '2021-05-09T13:11:31.788923+00:00', '--local', '--pool', 'default_pool', '--subdir', '/opt/airflow/airflow/example_dags/example_bash_operator.py']
[2021-05-09 13:11:34,756] {local_executor.py:81} INFO - QueuedLocalWorker running ['airflow', 'tasks', 'run', 'example_bash_operator', 'runme_0', '2021-05-09T13:11:31.788923+00:00', '--local', '--pool', 'default_pool', '--subdir', '/opt/airflow/airflow/example_dags/example_bash_operator.py']
[2021-05-09 13:11:34,757] {local_executor.py:386} INFO - Shutting down LocalExecutor; waiting for running tasks to finish.  Signal again if you don't want to wait.
[2021-05-09 13:11:34,817] {dagbag.py:487} INFO - Filling up the DagBag from /opt/airflow/airflow/example_dags/example_bash_operator.py
Running <TaskInstance: example_bash_operator.runme_0 2021-05-09T13:11:31.788923+00:00 [None]> on host 12c7fd58e084
```

**What you expected to happen**:

Pickled_id should get generated 

```
Pickled dag <DAG: example_bash_operator> as pickle_id: None
```

**How to reproduce it**:
run below command from command line in airflow environment
```
airflow tasks run example_bash_operator runme_0 now --ship-dag --interactive
```
**Would like to submit PR for this issue**: YES


airflow/cli/commands/task_command.py
"""
    Returns the unmet dependencies for a task instance from the perspective of the
    scheduler (i.e. why a task instance doesn't get scheduled and then queued by the
    scheduler, and then run by an executor).
    >>> airflow tasks failed-deps tutorial sleep 2015-01-01
    Task instance dependencies not met:
    Dagrun Running: Task instance's dagrun did not exist: Unknown reason
    Trigger Rule: Task's trigger rule 'all_success' requires all upstream tasks
    to have succeeded, but found 1 non-success(es).
    """
    dag = get_dag(args.subdir, args.dag_id)
    task = dag.get_task(task_id=args.task_id)
    ti = _get_ti(task, args.execution_date_or_run_id)
    dep_context = DepContext(deps=SCHEDULER_QUEUED_DEPS)
    failed_deps = list(ti.get_failed_dep_statuses(dep_context=dep_context))
    
    if failed_deps:
        print("Task instance dependencies not met:")
        for dep in failed_deps:
            print(f"{dep.dep_name}: {dep.reason}")
    else:
        print("Task instance dependencies are all met.")
@cli_utils.action_logging
@suppress_logs_and_warning
def task_state(args):