### 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)

"try_number": "1",
            "airflow_version": mock.ANY,
            "airflow_kpo_in_cluster": str(k.hook.is_in_cluster),
            "run_id": "test",
        }
    @patch(f"{POD_MANAGER_CLASS}.fetch_container_logs")
    @patch(f"{POD_MANAGER_CLASS}.await_container_completion", new=MagicMock)
    def test_no_handle_failure_on_success(self, fetch_container_mock):
        name_base = "test"
        k = KubernetesPodOperator(name=name_base, task_id="task")
        fetch_container_mock.return_value = None
        remote_pod_mock = MagicMock()
        remote_pod_mock.status.phase = "Succeeded"
        self.await_pod_mock.return_value = remote_pod_mock
        
        self.run_pod(k)
    def test_create_with_affinity(self):
        affinity = {
            "nodeAffinity": {
                "preferredDuringSchedulingIgnoredDuringExecution": [
                    {
                        "weight": 1,
                        "preference": {
                            "matchExpressions": [{"key": "disktype", "operator": "In", "values": ["ssd"]}]
                        },
                    }
                ]
            }
        }
        k = KubernetesPodOperator(