### Description

Make resources in KubernetesPodOperator as templated. We need to modify this during several runs and it needs code change for each run.

### Use case/motivation

For running CPU and memory intensive workloads, we want to continuously optimise the "limt_cpu" and "limit_memory" parameters. Hence, we want to provide these parameters as a part of the pipeline definition.

### Related issues

_No response_

### Are you willing to submit a PR?

- [X] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)

if task.has_dag():
        dag = task.dag
    else:
        dag = DAG(dag_id="dag", start_date=pendulum.now())
        dag.add_task(task)
    dag_run = DagRun(
        run_id=DagRun.generate_run_id(DagRunType.MANUAL, DEFAULT_DATE),
        run_type=DagRunType.MANUAL,
        dag_id=dag.dag_id,
    )
    task_instance = TaskInstance(task=task, run_id=dag_run.run_id)
    task_instance.dag_run = dag_run
    if map_index is not None:
        task_instance.map_index = map_index
    if persist_to_db:
        with create_session() as session:
            session.add(DagModel(dag_id=dag.dag_id))
            session.add(dag_run)
            session.add(task_instance)
            session.commit()
    return {
        "dag": dag,
        "ts": DEFAULT_DATE.isoformat(),
        "task": task,
        "ti": task_instance,
        "task_instance": task_instance,
        "run_id": "test",
    }
@pytest.mark.execution_timeout(300)
class TestKubernetesPodOperator: