### 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)

k8s_api_tolerations = [k8s.V1Toleration(key="key", operator="Equal", value="value")]
        tolerations = [{"key": "key", "operator": "Equal", "value": "value"}]
        k = KubernetesPodOperator(
            task_id="task",
            tolerations=tolerations,
        )
        pod = k.build_pod_request_obj(create_context(k))
        sanitized_pod = self.sanitize_for_serialization(pod)
        assert isinstance(pod.spec.tolerations[0], k8s.V1Toleration)
        assert sanitized_pod["spec"]["tolerations"] == tolerations
        k = KubernetesPodOperator(
            task_id="task",
            tolerations=k8s_api_tolerations,
        )
        pod = k.build_pod_request_obj(create_context(k))
        sanitized_pod = self.sanitize_for_serialization(pod)
        assert isinstance(pod.spec.tolerations[0], k8s.V1Toleration)
        assert sanitized_pod["spec"]["tolerations"] == tolerations
    def test_node_selector(self):