def report_correctness(original_outputs: _np.ndarray, final_outputs: _np.ndarray, log_prefix: str):
        """ Report PSNR values across two compatible tensors.
        This util is from https://github.com/apple/ml-stable-diffusion/blob/main/python_coreml_stable_diffusion/torch2coreml.py#L80,
        with a slightly modification.
        """
        ABSOLUTE_MIN_PSNR = 35

        _, original_psnr = compute_snr_and_psnr(original_outputs, original_outputs)
        _, final_psnr = compute_snr_and_psnr(original_outputs, final_outputs)

        dB_change = final_psnr - original_psnr
        _logger.info(
            f"{log_prefix}: PSNR changed by {dB_change:.1f} dB ({original_psnr:.1f} -> {final_psnr:.1f})"
        )

        if final_psnr < ABSOLUTE_MIN_PSNR:
            _logger.warning(f"{final_psnr:.1f} dB is low!")
        else:
            _logger.info(
                f"{final_psnr:.1f} dB > {ABSOLUTE_MIN_PSNR} dB (minimum allowed) parity check passed"
            )
        return final_psnr
 Report PSNR values across two compatible tensors.
        This util is from https://github.com/apple/ml-stable-diffusion/blob/main/python_coreml_stable_diffusion/torch2coreml.py#L80,
        with a slightly modification.
        
coremltools/models/utils.py