### 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)

k = KubernetesPodOperator(
            namespace="default",
            image="ubuntu:16.04",
            cmds=["bash", "-cx"],
            labels={"foo": "bar"},
            name="test",
            task_id="task",
            in_cluster=False,
            do_xcom_push=False,
        )
        self.run_pod(k)
        _, kwargs = k.client.list_namespaced_pod.call_args
        assert kwargs["label_selector"] == (
            "dag_id=dag,kubernetes_pod_operator=True,run_id=test,task_id=task,"
            "already_checked!=True,!airflow-worker"
        )
    def test_image_pull_secrets_correctly_set(self):