code: def ref_symmetric_hit_rate(
      features_a, features_b, logit_scale, top_ks=[1, 5, 10]
  ):
      assert len(features_a) == len(features_b)
      hit_rate = 0
      logits_per_a = (
          (logit_scale * features_a @ features_b.t()).detach().cpu()
      )
      logits_per_b = logits_per_a.t().detach().cpu()
      num_elements = len(features_a)
      for logits in [logits_per_a, logits_per_b]:
          preds = logits.reshape(-1)
          indexes = torch.broadcast_to(
              torch.arange(num_elements).reshape(-1, 1),
              (num_elements, num_elements),
          ).reshape(-1)
          target = torch.eye(num_elements, dtype=bool).reshape(-1)
          for k in top_ks:
              hr_k = RetrievalHitRate(top_k=k)
              hit_rate += hr_k(preds, target, indexes=indexes)
      return hit_rate / (2 * len(top_ks))
  
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 6 for calling a global function with example usage: ref_symmetric_hit_rate(features_a, features_b, logit_scale, top_ks) and returns: unknown