### 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)

nodeAffinity:
                  requiredDuringSchedulingIgnoredDuringExecution:
                    nodeSelectorTerms:
                    - matchExpressions:
                      - key: kubernetes.io/role
                        operator: In
                        values:
                        - foo
                        - bar
                  preferredDuringSchedulingIgnoredDuringExecution:
                  - weight: 1
                    preference:
                      matchExpressions:
                      - key: kubernetes.io/role
                        operator: In
                        values:
                        - foo
                        - bar
              containers:
                - name: base
                  image: ubuntu:16.04
                  imagePullPolicy: Always
                  command:
                    - something
        """
        tpl_file = tmp_path / "template.yaml"
        tpl_file.write_bytes(pod_template_yaml)
        yield tpl_file
    @pytest.mark.parametrize(("randomize_name",), ([True], [False]))
    def test_pod_template_file(self, randomize_name, pod_template_file):