code: def _dcorr(y, n2, A, dcov2_xx):
      """Helper function for distance correlation bootstrapping."""
      # Pairwise Euclidean distances
      b = squareform(pdist(y, metric="euclidean"))
      # Double centering
      B = (
          b
          - b.mean(axis=0)[None, :]
          - b.mean(axis=1)[:, None]
          + b.mean()
      )
      # Compute squared distance covariances
      dcov2_yy = np.vdot(B, B) / n2
      dcov2_xy = np.vdot(A, B) / n2
      return np.sqrt(dcov2_xy) / np.sqrt(
          np.sqrt(dcov2_xx) * np.sqrt(dcov2_yy)
      )
  
imports: import warnings
  import numpy
  import pandas
  import pandas_flavor
  from scipy.spatial.distance import pdist, squareform
  from scipy.stats import pearsonr, spearmanr, kendalltau
  from pingouin.config import options
  from pingouin.power import power_corr
  from pingouin.multicomp import multicomp
  from pingouin.effsize import compute_esci
  from pingouin.utils import remove_na, _perm_pval, _postprocess_dataframe
  from pingouin.bayesian import bayesfactor_pearson
label_desc: Use image node_id 12 for calling a global function with example usage: _dcorr(y, n2, A, dcov2_xx) and returns: unknown