code: def _transform(self, X, X2=None):
      """Compute distance/kernel matrix.
  
      private _transform containing core logic, called from transform
  
      Behaviour: returns pairwise distance/kernel matrix
          between samples in X and X2 (equal to X if not passed)
  
      Parameters
      ----------
      X : guaranteed to be Series or Panel of mtype X_inner_mtype, n instances
          if X_inner_mtype is list, _transform must support all types in it
          Data to be transformed
      X2 : guaranteed to be Series or Panel of mtype X_inner_mtype, m instances
          if X_inner_mtype is list, _transform must support all types in it
          Data to be transformed
          default X2 = X
  
      Returns
      -------
      distmat: np.array of shape [n, m]
          (i,j)-th entry contains distance/kernel between X[i] and X2[j]
      """
      estimator = self._get_delegate()
      return estimator.transform(X, X2=X2)
  
imports: from sktime.dists_kernels.base import BasePairwiseTransformerPanel
label_desc: Use image node_id 2 for calling the _DelegatedPairwiseTransformerPanel obj's underlying member method code with example usage: obj._transform(X, X2) and returns: estimator