code: def test_benchmark_set__filter_metrics(
      benchmark_set: BenchmarkSet,
      expected_metrics: pd.DataFrame,
  ) -> None:
      metrics = pd.DataFrame(
          [
              (0, "tiny_linear", "gpr", False, False, 0),
              (1, "tiny_sine", "gpr", False, False, 0),
              (2, "tiny_linear", "svgp", False, False, 0),
              (3, "tiny_linear", "gpr", True, False, 0),
              (4, "tiny_linear", "gpr", False, True, 0),
              (5, "tiny_linear", "gpr", False, False, 1),
              (6, "tiny_linear", "gpr", False, False, 2),
              (7, "tiny_linear", "gpr", False, False, 3),
          ],
          columns=[
              "id",
              "dataset",
              "model",
              "do_compile",
              "do_optimise",
              "repetition",
          ],
      )
      after = benchmark_set.filter_metrics(metrics).reset_index(
          drop=True
      )
      pd.testing.assert_frame_equal(expected_metrics, after)
  
file_path: GPflow/tests/benchmark/test_benchmark_api.py
imports: from typing import Collection
  from unittest.mock import Mock
  import pandas
  import pytest
  import benchmark.datasets
  import benchmark.models
  import benchmark.plotters
  from benchmark.benchmark_api import BenchmarkSet, BenchmarkSuite, BenchmarkTask
  from benchmark.grouping import GroupingKey
  from benchmark.grouping import GroupingSpec
label_desc: Use image node_id 2 for calling a global function with example usage: test_benchmark_set__filter_metrics(benchmark_set, expected_metrics) without return types