class_docstr: CVNet
  
  Args:
      resnet_depth (`int`):
          ResNet depth.
      reduction_dim (`int`):
          Reduction dimension of ResNet.
code: def extract_score_with_featuremap(self, query_feats, key_feats):
      with torch.no_grad():
          corr_qk = Correlation.build_crossscale_correlation(
              query_feats[0], key_feats[0], self.scales, self.conv2ds
          )
          logits_qk = self.cv_learner(corr_qk)
          score = self.softmax(logits_qk)[0][1]
      return score
  
file_path: towhee/towhee/models/cvnet/cvnet.py
imports: from functools import reduce
  from operator import add
  import torch
  from torch import nn
  from towhee.models.cvnet.cvnet_utils import extract_feat_res_pycls, get_configs
  from towhee.models.cvnet.cvnet_block import CVLearner, Correlation
  from towhee.models.cvnet.resnet import ResNet
  from towhee.models.utils import create_model
label_desc: Use image node_id 5 for calling the CVNet obj's underlying member method code with example usage: obj.extract_score_with_featuremap(query_feats, key_feats) and returns: score