How to use the niworkflows.NIWORKFLOWS_LOG.info function in niworkflows

def _post_run_hook(self, runtime):
        """ generates a report showing slices from each axis """

        if len(self.inputs.mask_files) != 1:
            raise ValueError(
                "ACompCorRPT only supports a single input mask. "
                "A list %s was found." % self.inputs.mask_files
            )
        self._anat_file = self.inputs.realigned_file
        self._mask_file = self.inputs.mask_files[0]
        self._seg_files = self.inputs.mask_files
        self._masked = False

        NIWORKFLOWS_LOG.info(
            'Generating report for aCompCor. file "%s", mask "%s"',
            self.inputs.realigned_file,
            self._mask_file,
        )

        return super(ACompCorRPT, self)._post_run_hook(runtime)

def _post_run_hook(self, runtime):
        """ generates a report showing slices from each axis of an arbitrary
        volume of in_file, with the resulting binary brain mask overlaid """

        self._anat_file = self.inputs.in_file
        self._mask_file = self.aggregate_outputs(runtime=runtime).mask_file
        self._seg_files = [self._mask_file]
        self._masked = True

        NIWORKFLOWS_LOG.info(
            'Generating report for nilearn.compute_epi_mask. file "%s", and mask file "%s"',
            self._anat_file,
            self._mask_file,
        )

        return super(ComputeEPIMask, self)._post_run_hook(runtime)

def _post_run_hook(self, runtime):
        """ there is not inner interface to run """
        self._fixed_image_label = self.inputs.after_label
        self._moving_image_label = self.inputs.before_label
        self._fixed_image = self.inputs.after
        self._moving_image = self.inputs.before
        self._contour = self.inputs.wm_seg if isdefined(self.inputs.wm_seg) else None
        NIWORKFLOWS_LOG.info(
            "Report - setting before (%s) and after (%s) images",
            self._fixed_image,
            self._moving_image,
        )

        return super(SimpleBeforeAfterRPT, self)._post_run_hook(runtime)

interface_result = self.norm.run()

            if interface_result.runtime.returncode != 0:
                NIWORKFLOWS_LOG.warning("Retry #%d failed.", self.retry)
                # Save outputs (if available)
                term_out = _write_outputs(
                    interface_result.runtime, ".nipype-%04d" % self.retry
                )
                if term_out:
                    NIWORKFLOWS_LOG.warning(
                        "Log of failed retry saved (%s).", ", ".join(term_out)
                    )
            else:
                runtime.returncode = 0
                # Note this in the log.
                NIWORKFLOWS_LOG.info(
                    "Successful spatial normalization (retry #%d).", self.retry
                )
                # Break out of the retry loop.
                return runtime

            self.retry += 1

        # If all tries fail, raise an error.
        raise RuntimeError(
            "Robust spatial normalization failed after %d retries." % (self.retry - 1)
        )

def _post_run_hook(self, runtime):
        self._fixed_image = self.inputs.fixed_image[0]
        self._moving_image = self.aggregate_outputs(runtime=runtime).warped_image
        NIWORKFLOWS_LOG.info(
            "Report - setting fixed (%s) and moving (%s) images",
            self._fixed_image,
            self._moving_image,
        )

        return super(ANTSRegistrationRPT, self)._post_run_hook(runtime)