code: def sqize_kernel(K, L, theta=1.0, normalize=False):
      """Compute the sequential kernel from a pairwise kernel matrix.
  
      aka compute "sequentialization of the kernel K"
  
      Parameters
      ----------
      K : 2D np.ndarray
          the kernel matrix of increments, i.e.,
          K[i,j] is the kernel between the i-th increment of path 1 (rows),
          and the j-th increment of path 2 (columns)
      L : an integer >= 1, representing the level of truncation
      theta : float, optional, default=1.0
          a positive scaling factor for the levels, i-th level is scaled by theta^i
      normalize : bool, optional, default = False
          whether the output kernel matrix is normalized
          if True, sums and cumsums are divided by prod(K.shape)
  
      Returns
      -------
      a real number, the sequential kernel between path 1 (rows of K) and path 2 (cols)
      """
      # L-1 runs through loop;
      # returns R_ij=(1+\sum_i2>i,j2>j A_i2,j2(1+\sum A_iLjL)...)
      if normalize:
          normfac = np.prod(K.shape)
          Id = np.ones(K.shape)
          R = np.ones(K.shape)
          for _ in range(L - 1):
              R = (Id + theta * cumsum_rev(K * R) / normfac) / (
                  1 + theta
              )
          return (1 + theta * np.sum(K * R) / normfac) / (1 + theta)
      else:
          Id = np.ones(K.shape)
          R = np.ones(K.shape)
          for _ in range(L - 1):
              R = Id + cumsum_rev(K * R)  # A*R is componentwise
          # outermost bracket: since i1>=1 and not i1>1 we do it outside of loop
          return 1 + np.sum(K * R)
  
imports: import collections
  from functools import partial
  import numpy
  from scipy.sparse.linalg import svds
  from sklearn.base import BaseEstimator, TransformerMixin
  from sktime.dists_kernels.base import BasePairwiseTransformerPanel
label_desc: Use image node_id 17 for calling a global function with example usage: sqize_kernel(K, L, theta, normalize) and returns: unknown, unknown