code: def test_symmetric_hit_rate():
      generator = torch.Generator()
      generator.manual_seed(0)
      for repeat in range(3):
          for top_ks in [[1, 5, 10], [20], [3, 7, 9]]:
              features_a = torch.randn(50, 2, generator=generator)
              features_b = torch.randn(50, 2, generator=generator)
              hit_rate_impl = compute_hit_rate(
                  features_a, features_b, logit_scale=1.0, top_ks=top_ks
              )
              hit_rate_ref = ref_symmetric_hit_rate(
                  features_a, features_b, logit_scale=1.0, top_ks=top_ks
              )
              assert (
                  pytest.approx(hit_rate_impl.item())
                  == hit_rate_ref.item()
              )
  
imports: import os
  import random
  import shutil
  import tempfile
  import pytest
  import torch
  from sklearn.metrics import f1_score, log_loss
  from torchmetrics import MeanMetric, RetrievalHitRate
  import autogluon.core.metrics
  from autogluon.multimodal import MultiModalPredictor
  from autogluon.multimodal.constants import MULTICLASS, Y_PRED, Y_TRUE
  from autogluon.multimodal.optimization.utils import compute_hit_rate, get_loss_func, get_metric
  from autogluon.multimodal.utils import compute_score, infer_metrics
  from ..utils.unittest_datasets import HatefulMeMesDataset, PetFinderDataset
  from ..utils.utils import get_home_dir
label_desc: Use image node_id 7 for calling a global function with example usage: test_symmetric_hit_rate() without return types