### 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)

context=context,
            )
            
            remote_pod = self.find_pod(self.pod.metadata.namespace, context=context)
            self.await_pod_start(pod=self.pod)
            if self.get_logs:
                self.pod_manager.fetch_container_logs(
                    pod=self.pod,
                    container_name=self.BASE_CONTAINER_NAME,
                    follow=True,
                )
            else:
                self.pod_manager.await_container_completion(
                    pod=self.pod, container_name=self.BASE_CONTAINER_NAME
                )
            if self.do_xcom_push:
                self.pod_manager.await_xcom_sidecar_container_start(pod=self.pod)
                result = self.extract_xcom(pod=self.pod)
            remote_pod = self.pod_manager.await_pod_completion(self.pod)
        finally:
            self.cleanup(
                pod=self.pod or self.pod_request_obj,
                remote_pod=remote_pod,
            )
        ti = context["ti"]
        ti.xcom_push(key="pod_name", value=self.pod.metadata.name)
        ti.xcom_push(key="pod_namespace", value=self.pod.metadata.namespace)
        if self.do_xcom_push:
            return result
    def cleanup(self, pod: k8s.V1Pod, remote_pod: k8s.V1Pod):