### 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)

task_id="task",
            affinity=affinity,
        )
        pod = k.build_pod_request_obj(create_context(k))
        sanitized_pod = self.sanitize_for_serialization(pod)
        assert isinstance(pod.spec.affinity, k8s.V1Affinity)
        assert sanitized_pod["spec"]["affinity"] == affinity
        k8s_api_affinity = k8s.V1Affinity(
            node_affinity=k8s.V1NodeAffinity(
                preferred_during_scheduling_ignored_during_execution=[
                    k8s.V1PreferredSchedulingTerm(
                        weight=1,
                        preference=k8s.V1NodeSelectorTerm(
                            match_expressions=[
                                k8s.V1NodeSelectorRequirement(key="disktype", operator="In", values=["ssd"])
                            ]
                        ),
                    )
                ]
            ),
        )
        k = KubernetesPodOperator(
            task_id="task",
            affinity=k8s_api_affinity,
        )
        pod = k.build_pod_request_obj(create_context(k))
        sanitized_pod = self.sanitize_for_serialization(pod)
        assert isinstance(pod.spec.affinity, k8s.V1Affinity)
        assert sanitized_pod["spec"]["affinity"] == affinity
    def test_tolerations(self):