### 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)

name_base = "test"
        k = KubernetesPodOperator(
            name=name_base,
            random_name_suffix=randomize,
            task_id="task",
        )
        context = create_context(k)
        pod = k.build_pod_request_obj(context)
        if randomize:
            assert pod.metadata.name.startswith(name_base)
            assert pod.metadata.name != name_base
        else:
            assert pod.metadata.name == name_base
    @pytest.fixture
    def pod_spec(self):
        return k8s.V1Pod(
            metadata=k8s.V1ObjectMeta(name="hello", labels={"foo": "bar"}, namespace="podspecnamespace"),
            spec=k8s.V1PodSpec(
                containers=[
                    k8s.V1Container(
                        name="base",
                        image="ubuntu:16.04",
                        command=["something"],
                    )
                ]
            ),
        )
    @pytest.mark.parametrize(("randomize_name",), ([True], [False]))
    def test_full_pod_spec(self, randomize_name, pod_spec):