### 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)

)
        
        
        if "node_selectors" in kwargs:
            raise ValueError(
                "Param `node_selectors` supplied. This param is no longer supported. "
                "Use `node_selector` instead."
            )
        super().__init__(**kwargs)
        self.kubernetes_conn_id = kubernetes_conn_id
        self.do_xcom_push = do_xcom_push
        self.image = image
        self.namespace = namespace
        self.cmds = cmds or []
        self.arguments = arguments or []
        self.labels = labels or {}
        self.startup_timeout_seconds = startup_timeout_seconds
        self.env_vars = convert_env_vars(env_vars) if env_vars else []
        if pod_runtime_info_envs:
            self.env_vars.extend([convert_pod_runtime_info_env(p) for p in pod_runtime_info_envs])
        self.env_from = env_from or []
        if configmaps:
            self.env_from.extend([convert_configmap(c) for c in configmaps])
        self.ports = [convert_port(p) for p in ports] if ports else []
        self.volume_mounts = [convert_volume_mount(v) for v in volume_mounts] if volume_mounts else []
        self.volumes = [convert_volume(volume) for volume in volumes] if volumes else []
        self.secrets = secrets or []
        self.in_cluster = in_cluster
        self.cluster_context = cluster_context
        self.reattach_on_restart = reattach_on_restart