How to use the niworkflows.interfaces.nibabel.load function in niworkflows

def _run_interface(self, runtime):
        img = nb.load(self.inputs.in_file)
        msknii = nb.load(self.inputs.in_mask)
        msk = msknii.get_fdata() > self.inputs.threshold

        self._results["out_file"] = fname_presuffix(
            self.inputs.in_file, suffix="_masked", newpath=runtime.cwd
        )

        if img.dataobj.shape[:3] != msk.shape:
            raise ValueError("Image and mask sizes do not match.")

        if not np.allclose(img.affine, msknii.affine):
            raise ValueError("Image and mask affines are not similar enough.")

        if img.dataobj.ndim == msk.ndim + 1:
            msk = msk[..., np.newaxis]

        masked = img.__class__(img.dataobj * msk, None, img.header)

def _run_interface(self, runtime):
        img = nb.load(self.inputs.in_file)

        self._results["out_file"] = fname_presuffix(
            self.inputs.in_file, suffix="_masked", newpath=runtime.cwd
        )
        self._results["out_mask"] = fname_presuffix(
            self.inputs.in_file, suffix="_mask", newpath=runtime.cwd
        )

        data = img.get_fdata()
        mask = data > self.inputs.thresh_low
        data[~mask] = 0.0
        masked = img.__class__(data, img.affine, img.header)
        masked.to_filename(self._results["out_file"])

        img.header.set_data_dtype("uint8")
        maskimg = img.__class__(mask.astype("uint8"), img.affine, img.header)

def _run_interface(self, runtime):
        nii_list = []
        for f in self.inputs.in_files:
            filenii = nb.squeeze_image(nb.load(f))
            ndim = filenii.dataobj.ndim
            if ndim == 3:
                nii_list.append(filenii)
                continue
            elif self.inputs.allow_4D and ndim == 4:
                nii_list += nb.four_to_three(filenii)
                continue
            else:
                raise ValueError(
                    "Input image has an incorrect number of dimensions" f" ({ndim})."
                )

        img_4d = nb.concat_images(nii_list)
        out_file = fname_presuffix(
            self.inputs.in_files[0], suffix="_merged", newpath=runtime.cwd
        )

def _run_interface(self, runtime):
        in_file = self.inputs.in_file
        img = nb.load(in_file)
        extra_dims = tuple(dim for dim in img.shape[3:] if dim > 1) or (1,)
        if len(extra_dims) != 1:
            raise ValueError(f"Invalid shape {'x'.join(str(s) for s in img.shape)}")
        img = img.__class__(
            img.dataobj.reshape(img.shape[:3] + extra_dims), img.affine, img.header
        )

        self._results["out_files"] = []
        for i, img_3d in enumerate(nb.four_to_three(img)):
            out_file = fname_presuffix(
                in_file, suffix=f"_idx-{i:03}", newpath=runtime.cwd
            )
            img_3d.to_filename(out_file)
            self._results["out_files"].append(out_file)

        return runtime

def _run_interface(self, runtime):
        img = nb.load(self.inputs.in_file)
        msknii = nb.load(self.inputs.in_mask)
        msk = msknii.get_fdata() > self.inputs.threshold

        self._results["out_file"] = fname_presuffix(
            self.inputs.in_file, suffix="_masked", newpath=runtime.cwd
        )

        if img.dataobj.shape[:3] != msk.shape:
            raise ValueError("Image and mask sizes do not match.")

        if not np.allclose(img.affine, msknii.affine):
            raise ValueError("Image and mask affines are not similar enough.")

        if img.dataobj.ndim == msk.ndim + 1:
            msk = msk[..., np.newaxis]