### 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)

"""
        When pod creation fails, we never get a read of the remote pod.  In this case we don't attempt
        to delete the pod.
        """
        k = KubernetesPodOperator(
            task_id="task",
            is_delete_operator_pod=True,
        )
        if should_fail:
            self.create_mock.side_effect = AirflowException("fake failure")
        else:
            await_pod_mock.return_value.status.phase = "Succeeded"
        cm = pytest.raises(AirflowException) if should_fail else nullcontext()
        with cm:
            self.run_pod(k)
        if should_fail:
            delete_pod_mock.assert_not_called()
        else:
            delete_pod_mock.assert_called()
    @pytest.mark.parametrize("randomize", [True, False])
    def test_provided_pod_name(self, randomize):