docs: def _connect_pipeline(self, pipeline, required_outputs, workflow,
                            subject_inds, visit_inds, filter_array, force=False):
          """
          Connects a pipeline to a overarching workflow that sets up iterators
          over subjects|visits present in the repository (if required) and
          repository source and sink nodes
  
          Parameters
          ----------
          pipeline : Pipeline
              The pipeline to connect
          required_outputs : set[str] | None
              The outputs required to be produced by this pipeline. If None all
              are deemed to be required
          workflow : nipype.pipeline.engine.Workflow
              The overarching workflow to connect the pipeline to
          subject_inds : dct[str, int]
              A mapping of subject ID to row index in the filter array
          visit_inds : dct[str, int]
              A mapping of visit ID to column index in the filter array
          filter_array : 2-D numpy.array[bool]
              A two-dimensional boolean array, where rows correspond to
              subjects and columns correspond to visits in the repository. True
              values represent a combination of subject & visit ID to include
              in the current round of processing. Note that if the 'force'
              flag is not set, sessions won't be reprocessed unless the
              save provenance doesn't match that of the given pipeline.
          force : bool | 'all'
              A flag to force the processing of all sessions in the filter
              array, regardless of whether the parameters|pipeline used
              to generate existing data matches the given pipeline
          """
          if self.reprocess == 'force':
              force = True
          # Close-off construction of the pipeline and created, input and output
          # nodes and provenance dictionary
          pipeline.cap()
          # Prepend prerequisite pipelines to complete workflow if they need
          # to be (re)processed
          final_nodes = []
          # The array that represents the subject/visit pairs for which any
          # prerequisite pipeline will be (re)processed, and which therefore
          # needs to be included in the processing of the current pipeline. Row
          # indices correspond to subjects and column indices visits
          prqs_to_process_array = np.zeros((len(subject_inds), len(visit_inds)),
                                           dtype=bool)
          # The array that represents the subject/visit pairs for which any
          # prerequisite pipeline will be skipped due to missing inputs. Row
          # indices correspond to subjects and column indices visits
          prqs_to_skip_array = np.zeros((len(subject_inds), len(visit_inds)),
                                        dtype=bool)
          for getter_name in pipeline.prerequisites:
              prereq = pipeline.study.pipeline(getter_name)
              if prereq.to_process_array.any():
                  final_nodes.append(prereq.node('final'))
                  prqs_to_process_array |= prereq.to_process_array
              prqs_to_skip_array |= prereq.to_skip_array
          # Get list of sessions that need to be processed (i.e. if
          # they don't contain the outputs of this pipeline)
          to_process_array, to_protect_array, to_skip_array = self._to_process(
              pipeline, required_outputs, prqs_to_process_array,
              prqs_to_skip_array, filter_array, subject_inds, visit_inds, force)
          # Store the arrays signifying which nodes to process, protect or skip
          # so they can be passed to downstream pipelines
          pipeline.to_process_array = to_process_array
          pipeline.to_protect_array = to_protect_array
          pipeline.to_skip_array = to_skip_array
          # Check to see if there are any sessions to process
          if not to_process_array.any():
              raise ArcanaNoRunRequiredException(
                  "No sessions to process for '{}' pipeline"
                  .format(pipeline.name))
          # Set up workflow to run the pipeline, loading and saving from the
          # repository
          workflow.add_nodes([pipeline._workflow])
          # If prerequisite pipelines need to be processed, connect their
          # "final" nodes to the initial node of this pipeline to ensure that
          # they are all processed before this pipeline is run.
          if final_nodes:
              prereqs = pipeline.add('prereqs', Merge(len(final_nodes)))
              for i, final_node in enumerate(final_nodes, start=1):
                  workflow.connect(final_node, 'out', prereqs, 'in{}'.format(i))
          else:
              prereqs = None
          # Construct iterator structure over subjects and sessions to be
          # processed
          iter_nodes = self._iterate(pipeline, to_process_array, subject_inds,
                                     visit_inds)
          sources = {}
          # Loop through each frequency present in the pipeline inputs and
          # create a corresponding source node
          for freq in pipeline.input_frequencies:
              try:
                  inputs = list(pipeline.frequency_inputs(freq))
              except ArcanaMissingDataException as e:
                  raise ArcanaMissingDataException(
                      str(e) + ", which is required for pipeline '{}'".format(
                          pipeline.name))
              inputnode = pipeline.inputnode(freq)
              sources[freq] = source = pipeline.add(
                  '{}_source'.format(freq),
                  RepositorySource(
                      i.collection for i in inputs),
                  inputs=({'prereqs': (prereqs, 'out')} if prereqs is not None
                           else {}))
              # Connect iter_nodes to source and input nodes
              for iterator in pipeline.iterators(freq):
                  pipeline.connect(iter_nodes[iterator], iterator, source,
                                   iterator)
                  pipeline.connect(source, iterator, inputnode,
                                   iterator)
              for input in inputs:  # @ReservedAssignment
                  pipeline.connect(source, input.suffixed_name,
                                   inputnode, input.name)
          deiter_nodes = {}
  
          def deiter_node_sort_key(it):
              """
              If there are two iter_nodes (i.e. both subject and visit ID) and
              one depends on the other (i.e. if the visit IDs per subject
              vary and vice-versa) we need to ensure that the dependent
              iterator is deiterated (joined) first.
              """
              return iter_nodes[it].itersource is None
  
          # Connect all outputs to the repository sink, creating a new sink for
          # each frequency level (i.e 'per_session', 'per_subject', 'per_visit',
          # or 'per_study')
          for freq in pipeline.output_frequencies:
              outputs = list(pipeline.frequency_outputs(freq))
              if pipeline.iterators(freq) - pipeline.iterators():
                  raise ArcanaDesignError(
                      "Doesn't make sense to output '{}', which are of '{}' "
                      "frequency, when the pipeline only iterates over '{}'"
                      .format("', '".join(o.name for o in outputs), freq,
                              "', '".join(pipeline.iterators())))
              outputnode = pipeline.outputnode(freq)
              # Connect filesets/fields to sink to sink node, skipping outputs
              # that are study inputs
              to_connect = {o.suffixed_name: (outputnode, o.name)
                            for o in outputs if o.is_spec}
              # Connect iterators to sink node
              to_connect.update(
                  {i: (iter_nodes[i], i) for i in pipeline.iterators()})
              # Connect checksums/values from sources to sink node in order to
              # save in provenance, joining where necessary
              for input_freq in pipeline.input_frequencies:
                  checksums_to_connect = [
                      i.checksum_suffixed_name
                      for i in pipeline.frequency_inputs(input_freq)]
                  if not checksums_to_connect:
                      # Rare case of a pipeline with no inputs only iter_nodes
                      # that will only occur in unittests in all likelihood
                      continue
                  # Loop over iterators that need to be joined, i.e. that are
                  # present in the input frequency but not the output frequency
                  # and create join nodes
                  source = sources[input_freq]
                  for iterator in (pipeline.iterators(input_freq) -
                                    pipeline.iterators(freq)):
                      join = pipeline.add(
                          '{}_to_{}_{}_checksum_join'.format(
                              input_freq, freq, iterator),
                          IdentityInterface(
                              checksums_to_connect),
                          inputs={
                              tc: (source, tc) for tc in checksums_to_connect},
                          joinsource=iterator,
                          joinfield=checksums_to_connect)
                      source = join
                  to_connect.update(
                      {tc: (source, tc) for tc in checksums_to_connect})
              # Add sink node
              sink = pipeline.add(
                  '{}_sink'.format(freq),
                  RepositorySink(
                      (o.collection for o in outputs), pipeline),
                  inputs=to_connect)
              # "De-iterate" (join) over iterators to get back to single child
              # node by the time we connect to the final node of the pipeline Set
              # the sink and subject_id as the default deiterator if there are no
              # deiterates (i.e. per_study) or to use as the upstream node to
              # connect the first deiterator for every frequency
              deiter_nodes[freq] = sink  # for per_study the "deiterator" == sink
              for iterator in sorted(pipeline.iterators(freq),
                                      key=deiter_node_sort_key):
                  # Connect to previous deiterator or sink
                  # NB: we only need to keep a reference to the last one in the
                  # chain in order to connect with the "final" node, so we can
                  # overwrite the entry in the 'deiter_nodes' dict
                  deiter_nodes[freq] = pipeline.add(
                      '{}_{}_deiter'.format(freq, iterator),
                      IdentityInterface(
                          ['checksums']),
                      inputs={
                          'checksums': (deiter_nodes[freq], 'checksums')},
                      joinsource=iterator,
                      joinfield='checksums')
          # Create a final node, which is used to connect with downstream
          # pipelines
          pipeline.add(
              'final',
              Merge(
                  len(deiter_nodes)),
              inputs={
                  'in{}'.format(i): (di, 'checksums')
                  for i, di in enumerate(deiter_nodes.values(), start=1)})