code: def evaluate(
      *,
      evaluator: Evaluator,
      mapped_triples: MappedTriples,
      batch_size: int,
      slice_size: int,
      progress_bar: tqdm,
      targets: Collection[Target],
      **kwargs,
  ) -> MetricResults:
      """
      Evaluate a model with the given evaluator.
  
      .. note ::
          this method is wrapped into two memory utilization maximizer, which reduce the parameters `batch_size` and
          `slice_size`, if necessary due to memory constraints.
  
      :param evaluator:
          the evaluator instance that is used for evaluation
      :param mapped_triples:
          the evaluation triples
      :param batch_size:
          the (maximum) batch size. It will be reduced when (GPU) out-of-memory problems occur.
      :param slice_size:
          the (maximum) slice size. It will be reduced when (GPU) out-of-memory problems occur, and batch size reduction
          could not resolve the issue.
      :param progress_bar:
          whether to display a progress bar
      :param targets:
          the evaluation targets
      :param kwargs:
          additional keyword-based parameters are passed to :meth:`_evaluate_batch`.
  
      :return:
          the evaluation result
      """
      # todo: maybe we want to have some more keys outside of kwargs for hashing / have more visibility about
      #  what is passed around
      # clear evaluator and reset progress bar (necessary for size-probing / evaluation fallback)
      evaluator.clear()
      progress_bar.reset(total=mapped_triples.shape[0])
  
      # batch-wise processing
      for batch in mapped_triples.split(split_size=batch_size):
          # the relation_filter can be re-used (per batch) when we evaluate head *and* tail predictions
          # (which is the standard setting), cf. create_sparse_positive_filter_
          relation_filter = None
          for target in targets:
              relation_filter = _evaluate_batch(
                  batch=batch,
                  target=target,
                  evaluator=evaluator,
                  relation_filter=relation_filter,
                  **kwargs,
                  slice_size=slice_size,
              )
          # update progress bar with actual batch size
          progress_bar.update(batch.shape[0])
      return evaluator.finalize()
  
imports: from __future__ import annotations
  import logging
  import timeit
  import warnings
  from abc import ABC, abstractmethod
  from collections import ChainMap
  from typing import Any, ClassVar, Collection, Generic, List, Mapping, NamedTuple, Optional, Tuple, Type, TypeVar, Union, cast
  import pandas
  import torch
  from torch_max_mem import maximize_memory_utilization
  from tqdm.autonotebook import tqdm
  from ..constants import COLUMN_LABELS, TARGET_TO_INDEX, TARGET_TO_KEY_LABELS
  from ..metrics.utils import Metric
  from ..models import Model
  from ..triples.triples_factory import restrict_triples
  from ..triples.utils import get_entities, get_relations
  from ..typing import LABEL_HEAD, LABEL_RELATION, LABEL_TAIL, InductiveMode, MappedTriples, Target
  from ..utils import flatten_dictionary, format_relative_comparison, normalize_string, prepare_filter_triples
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate() and returns: evaluator