code: def group_variants(
      candidates,
      truths,
      max_group_size=_MAX_GROUP_SIZE,
      max_separation=_MAX_SEPARATION_WITHIN_VARIANT_GROUP,
      max_gt_options_product=_MAX_GT_OPTIONS_PRODUCT,
      force_group_within_bp=_FORCE_GROUP_WITHIN_BP,
  ):
      """Splits candidate and truth variants into smaller groups if necessary.
  
      This function takes in a list of candidate and truth variants and splits up
      those lists into groups that respect the requirements of the max_group_size
      and max_separation arguments. This is necessary because the labeling algorithm
      is very expensive as a function of the number of input variants, so to avoid
      excessive runtime we break up our potentially large list of candidate and
      truth variants into smaller groups (max number controlled by max_group_size)
      based on a maximum distance allowed between the closest variants within the
      group.
  
      The current algorithm is a simple greedy one; we effectively merge the two
      variant lists together, make groups greedily on that list until either the
      maximum number of elements of a specific type (i.e., max_group_size of 2
      implies we can have up to two candidate variants or truth variants within a
      group) or we encounter a variant further away from the closest variant within
      the current group than allowed by max_separation.
  
      Args:
        candidates: list[nucleus.proto.Variant]. A sorted list of candidate variants
          on the same chromosome.
        truths: list[nucleus.proto.Variant]. A sorted list of truth variants on the
          same chromosome.
        max_group_size: int >= 0. The maximum number of variants of a specific type
          allowed within a group.
        max_separation: int >= 0. The maximum distance, in basepairs, allowed
          between the closest variants within a group.
        max_gt_options_product: int >= 0. The maximum number of combinations of
          genotypes (product of all genotypes in the group).
        force_group_within_bp: int >= 0. Variants within this many bps will be
          forced to be put in the same group. This is to ensure that we do not
          decouple candidates and truths in variant-dense regions. This value can be
          set to -1 for unit-test purposes. Setting -1 will not force any grouping
          of variants.
  
      Returns:
        A list of grouped variants in 2-tuples, such as:
  
          [(candidates1, truth_variants1), ...]
  
        where each tuple contains the candidate and truth variants for that group.
  
      Raises:
        ValueError: if any of the inputs are malformed.
      """
      if max_group_size < 0:
          raise ValueError(
              "max_group_size={} must be >= 0".format(max_group_size)
          )
      if max_separation < 0:
          raise ValueError(
              "max_separation={} must be >= 0".format(max_separation)
          )
      if max_gt_options_product < 0:
          raise ValueError(
              "max_gt_options_product={} must be >= 0".format(
                  max_gt_options_product
              )
          )
      _raise_if_not_sorted_or_not_on_same_chromosome(candidates)
      _raise_if_not_sorted_or_not_on_same_chromosome(truths)
  
      def _num_genotypes(variant):
          """For example, if there is 2 alts, there are 6 (==4*3/2) possible GTs."""
          num_ref_and_alts = len(variant.alternate_bases) + 1
          return (num_ref_and_alts + 1) * num_ref_and_alts / 2
  
      def to_grouped_variants(variants, candidate_type):
          """Converts a Variant proto to a _VariantToGroup tuple."""
          return [
              _VariantToGroup(v.start, candidate_type, v)
              for v in variants
          ]
  
      def _of_type(group, required_type):
          """Selects a list of Variant protos from list[_VariantToGroup] of type."""
          return [
              gv.variant for gv in group if gv.type == required_type
          ]
  
      def _split_grouped_variants(group):
          """Splits a list of _VariantToGroup into candidate and truth variants."""
          return _of_type(group, _CANDIDATE_MARKER), _of_type(
              group, _TRUTH_MARKER
          )
  
      def _include_in_variant_group(
          group, group_variant, new_gt_options_product
      ):
          if not group:
              return True
          if new_gt_options_product >= max_gt_options_product:
              logging.info(
                  (
                      "Not including more because genotype_options_product will "
                      "be %s, which exceeds max(=%s)"
                  ),
                  new_gt_options_product,
                  max_gt_options_product,
              )
              return False
          n_of_type = sum(
              1 for g in group if g.type == group_variant.type
          )
          if n_of_type >= max_group_size:
              return False
          else:
              return any(
                  group_variant.variant.start - g.variant.end + 1
                  <= max_separation
                  for g in group
              )
  
      # Convert our lists of variant protos into _VariantToGroup tuples compatible
      # with the heapq API (sorts on tuples), so we get a single iterable of
      # variants with marked types and sorted by start position (first element of
      # each tuple).
      groupable_variants = heapq.merge(
          to_grouped_variants(candidates, _CANDIDATE_MARKER),
          to_grouped_variants(truths, _TRUTH_MARKER),
      )
  
      # Go through out groupable_variants and split them up into groups according to
      # the predicate _include_in_variant_group.
      groups = []
      current_group = []
      current_gt_options_product = 1
      previous_pos_end = 0
      for group_variant in groupable_variants:
          new_gt_options_product = (
              current_gt_options_product
              * _num_genotypes(group_variant.variant)
          )
          distance_from_prev_variant = (
              group_variant.variant.end - previous_pos_end
          )
          if (
              _include_in_variant_group(
                  current_group, group_variant, new_gt_options_product
              )
              or distance_from_prev_variant <= force_group_within_bp
          ):
              current_group.append(group_variant)
              current_gt_options_product = new_gt_options_product
          else:
              groups.append(current_group)
              current_group = [group_variant]
              current_gt_options_product = _num_genotypes(
                  group_variant.variant
              )
          previous_pos_end = group_variant.variant.end
      if current_group:
          groups.append(current_group)
  
      # Finally split up each group into candidates and truths.
      return [_split_grouped_variants(g) for g in groups]
  
imports: import collections
  import copy
  import enum
  import heapq
  import itertools
  from absl import logging
  from deepvariant.labeler import variant_labeler
  from deepvariant.protos import deepvariant_pb2
  from third_party.nucleus.io import fasta
  from third_party.nucleus.util import ranges
  from third_party.nucleus.util import variant_utils
  from third_party.nucleus.util import variantcall_utils
label_desc: Use image node_id 2 for calling a global function with example usage: group_variants(candidates, truths, max_group_size, max_separation, max_gt_options_product, force_group_within_bp) and returns: unknown, unknown, unknown, unknown, _of_type,  _of_type, True, False, False, any