code: def distances(
      self, record_pairs: Sequence[RecordDictPair]
  ) -> numpy.typing.NDArray[numpy.float_]:
      num_records = len(record_pairs)
  
      distances = numpy.empty((num_records, len(self)), "f4")
  
      for i, (record_1, record_2) in enumerate(record_pairs):
          for field, compare, start, stop in self._field_comparators:
              if (
                  record_1[field] is not None
                  and record_2[field] is not None
              ):
                  distances[i, start:stop] = compare(
                      record_1[field], record_2[field]
                  )
              elif hasattr(compare, "missing"):
                  distances[i, start:stop] = compare(
                      record_1[field], record_2[field]
                  )
              else:
                  distances[i, start:stop] = numpy.nan
  
      distances = self._add_derived_distances(distances)
  
      return distances
  
imports: from __future__ import annotations
  import copyreg
  import pkgutil
  import types
  from typing import TYPE_CHECKING, cast
  import numpy
  import dedupe.variables
  from dedupe.variables.base import FieldType
  from dedupe.variables.base import MissingDataType, Variable
  from dedupe.variables.interaction import InteractionType
label_desc: Use image node_id 5 for calling the DataModel obj's underlying member method code with example usage: obj.distances(record_pairs) and returns: distances