### 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)

return ApiClient().sanitize_for_serialization(obj)
    @patch(HOOK_CLASS)
    def test_config_path(self, hook_mock):
        file_path = "/tmp/fake_file"
        k = KubernetesPodOperator(
            namespace="default",
            image="ubuntu:16.04",
            cmds=["bash", "-cx"],
            arguments=["echo 10"],
            labels={"foo": "bar"},
            name="test",
            task_id="task",
            do_xcom_push=False,
            config_file=file_path,
        )
        remote_pod_mock = MagicMock()
        remote_pod_mock.status.phase = "Succeeded"
        self.await_pod_mock.return_value = remote_pod_mock
        self.run_pod(k)
        hook_mock.assert_called_once_with(
            cluster_context=None,
            conn_id=None,
            config_file=file_path,
            in_cluster=None,
        )
    def test_env_vars(self):