### 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 we aren't deleting pods mark "checked" if the task completes (successful or otherwise)"""
        dag = DAG("hello2", start_date=pendulum.now())
        k = KubernetesPodOperator(
            task_id="task",
            is_delete_operator_pod=False,
            dag=dag,
        )
        remote_pod_mock = MagicMock()
        remote_pod_mock.status.phase = "Failed" if should_fail else "Succeeded"
        self.await_pod_mock.return_value = remote_pod_mock
        context = create_context(k, persist_to_db=True)
        if should_fail:
            with pytest.raises(AirflowException):
                k.execute(context=context)
        else:
            k.execute(context=context)
        mock_patch_already_checked.assert_called_once()
        mock_delete_pod.assert_not_called()
    def test_task_id_as_name(self):