### 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)

"labels",
        "config_file",
        "pod_template_file",
        "namespace",
    )
    template_fields_renderers = {"env_vars": "py"}
    def __init__(
        self,
        *,
        kubernetes_conn_id: str | None = None,  
        namespace: str | None = None,
        image: str | None = None,
        name: str | None = None,
        random_name_suffix: bool | None = True,
        cmds: list[str] | None = None,
        arguments: list[str] | None = None,
        ports: list[k8s.V1ContainerPort] | None = None,
        volume_mounts: list[k8s.V1VolumeMount] | None = None,
        volumes: list[k8s.V1Volume] | None = None,
        env_vars: list[k8s.V1EnvVar] | None = None,
        env_from: list[k8s.V1EnvFromSource] | None = None,
        secrets: list[Secret] | None = None,
        in_cluster: bool | None = None,
        cluster_context: str | None = None,
        labels: dict | None = None,
        reattach_on_restart: bool = True,
        startup_timeout_seconds: int = 120,
        get_logs: bool = True,
        image_pull_policy: str | None = None,
        annotations: dict | None = None,