Try fixing.

src/fmripost_aroma/workflows/base.py

@@ -428,12 +428,12 @@ def init_single_run_wf(bold_file):
 
     if ('bold_mni152nlin6asym' not in functional_cache) and ('bold_raw' in functional_cache):
         # Resample to MNI152NLin6Asym:res-2, for ICA-AROMA classification
-        from fmriprep.workflows.bold.apply import init_bold_volumetric_resample_wf
         from fmriprep.workflows.bold.stc import init_bold_stc_wf
         from niworkflows.interfaces.header import ValidateImage
         from templateflow.api import get as get_template
 
         from fmripost_aroma.interfaces.misc import ApplyTransforms
+        from fmripost_aroma.workflows.resample import init_bold_volumetric_resample_wf
 
         workflow.__desc__ += """\
 Raw BOLD series were resampled to MNI152NLin6Asym:res-2, for ICA-AROMA classification.

src/fmripost_aroma/workflows/resample.py

@@ -0,0 +1,189 @@
+from __future__ import annotations
+
+import nipype.interfaces.utility as niu
+import nipype.pipeline.engine as pe
+from fmriprep.interfaces.resampling import DistortionParameters, ResampleSeries
+from niworkflows.interfaces.nibabel import GenerateSamplingReference
+from niworkflows.interfaces.utility import KeySelect
+
+
+def init_bold_volumetric_resample_wf(
+    *,
+    metadata: dict,
+    mem_gb: dict[str, float],
+    jacobian: bool,
+    fieldmap_id: str | None = None,
+    omp_nthreads: int = 1,
+    name: str = 'bold_volumetric_resample_wf',
+) -> pe.Workflow:
+    """Resample a BOLD series to a volumetric target space.
+
+    This workflow collates a sequence of transforms to resample a BOLD series
+    in a single shot, including motion correction and fieldmap correction, if
+    requested.
+
+    .. workflow::
+
+        from fmripost_aroma.workflows.bold.resampling import init_bold_volumetric_resample_wf
+
+        wf = init_bold_volumetric_resample_wf(
+            metadata={
+                'RepetitionTime': 2.0,
+                'PhaseEncodingDirection': 'j-',
+                'TotalReadoutTime': 0.03
+            },
+            mem_gb={'resampled': 1},
+            jacobian=True,
+            fieldmap_id='my_fieldmap',
+        )
+
+    Parameters
+    ----------
+    metadata
+        BIDS metadata for BOLD file.
+    fieldmap_id
+        Fieldmap identifier, if fieldmap correction is to be applied.
+    omp_nthreads
+        Maximum number of threads an individual process may use.
+    name
+        Name of workflow (default: ``bold_volumetric_resample_wf``)
+
+    Inputs
+    ------
+    bold_file
+        BOLD series to resample.
+    bold_ref_file
+        Reference image to which BOLD series is aligned.
+    target_ref_file
+        Reference image defining the target space.
+    target_mask
+        Brain mask corresponding to ``target_ref_file``.
+        This is used to define the field of view for the resampled BOLD series.
+    motion_xfm
+        List of affine transforms aligning each volume to ``bold_ref_file``.
+        If undefined, no motion correction is performed.
+    fmap
+        Fieldmap image.
+    fmap_id
+        Fieldmap identifier, to select correct fieldmap in case there are multiple.
+    boldref2anat_xfm
+        Affine transform from ``bold_ref_file`` to the anatomical reference image.
+    anat2std_xfm
+        Affine transform from the anatomical reference image to standard space.
+        Leave undefined to resample to anatomical reference space.
+
+    Outputs
+    -------
+    bold_file
+        The ``bold_file`` input, resampled to ``target_ref_file`` space.
+    resampling_reference
+        An empty reference image with the correct affine and header for resampling
+        further images into the BOLD series' space.
+
+    """
+    workflow = pe.Workflow(name=name)
+
+    inputnode = pe.Node(
+        niu.IdentityInterface(
+            fields=[
+                'bold_file',
+                'bold_ref_file',
+                'target_ref_file',
+                'target_mask',
+                # HMC
+                'motion_xfm',
+                # SDC
+                'fmap',
+                'fmap_id',
+                # Anatomical
+                'boldref2anat_xfm',
+                # Template
+                'anat2std_xfm',
+                # Entity for selecting target resolution
+                'resolution',
+            ],
+        ),
+        name='inputnode',
+    )
+
+    outputnode = pe.Node(
+        niu.IdentityInterface(fields=['bold_file', 'resampling_reference']),
+        name='outputnode',
+    )
+
+    gen_ref = pe.Node(GenerateSamplingReference(), name='gen_ref', mem_gb=0.3)
+
+    boldref2target = pe.Node(niu.Merge(2), name='boldref2target', run_without_submitting=True)
+    bold2target = pe.Node(niu.Merge(2), name='bold2target', run_without_submitting=True)
+    resample = pe.Node(
+        ResampleSeries(jacobian=jacobian),
+        name='resample',
+        n_procs=omp_nthreads,
+        mem_gb=mem_gb['resampled'],
+    )
+
+    workflow.connect([
+        (inputnode, gen_ref, [
+            ('bold_ref_file', 'moving_image'),
+            ('target_ref_file', 'fixed_image'),
+            ('target_mask', 'fov_mask'),
+            (('resolution', _is_native), 'keep_native'),
+        ]),
+        (inputnode, boldref2target, [
+            ('boldref2anat_xfm', 'in1'),
+            ('anat2std_xfm', 'in2'),
+        ]),
+        (inputnode, bold2target, [('motion_xfm', 'in1')]),
+        (inputnode, resample, [('bold_file', 'in_file')]),
+        (gen_ref, resample, [('out_file', 'ref_file')]),
+        (boldref2target, bold2target, [('out', 'in2')]),
+        (bold2target, resample, [('out', 'transforms')]),
+        (gen_ref, outputnode, [('out_file', 'resampling_reference')]),
+        (resample, outputnode, [('out_file', 'bold_file')]),
+    ])  # fmt:skip
+
+    if not fieldmap_id:
+        return workflow
+
+    fmap_select = pe.Node(
+        KeySelect(fields=['fmap'], key=fieldmap_id),
+        name='fmap_select',
+        run_without_submitting=True,
+    )
+    distortion_params = pe.Node(
+        DistortionParameters(metadata=metadata),
+        name='distortion_params',
+        run_without_submitting=True,
+    )
+    workflow.connect([
+        (inputnode, fmap_select, [
+            ('fmap', 'fmap_ref'),
+            ('fmap_id', 'keys'),
+        ]),
+        (inputnode, distortion_params, [('bold_file', 'in_file')]),
+        # Inject fieldmap correction into resample node
+        (fmap_select, resample, [('fmap', 'fieldmap')]),
+        (distortion_params, resample, [
+            ('readout_time', 'ro_time'),
+            ('pe_direction', 'pe_dir'),
+        ]),
+    ])  # fmt:skip
+
+    return workflow
+
+
+def _gen_inverses(inlist: list) -> list[bool]:
+    """Create a list indicating the first transform should be inverted.
+
+    The input list is the collection of transforms that follow the
+    inverted one.
+    """
+    from niworkflows.utils.connections import listify
+
+    if not inlist:
+        return [True]
+    return [True] + [False] * len(listify(inlist))
+
+
+def _is_native(value):
+    return value == 'native'