keras/src/backend/numpy/trainer.py/NumpyTrainer/one_test_step
class NumpyTrainer:    def make_test_function(self, force=False):
        if self.test_function is not None and not force:
            return self.test_function

        def one_test_step(data):
            data = data[0]
            return self.test_step(data)

        def multi_test_steps(data):
            for single_step_data in data:
                logs = one_test_step([single_step_data])
            return logs

        if self.steps_per_execution > 1:
            test_step = multi_test_steps
        else:
            test_step = one_test_step

        self.test_function = test_step