code: def _gak(gram, be=None):
      """Compute Global Alignment Kernel (GAK) between (possibly
      multidimensional) time series and return it.
  
      Parameters
      ----------
      gram : array-like, shape=(sz1, sz2)
          Gram matrix.
      be : Backend object or string or None
          Backend. If `be` is an instance of the class `NumPyBackend` or the string `"numpy"`,
          the NumPy backend is used.
          If `be` is an instance of the class `PyTorchBackend` or the string `"pytorch"`,
          the PyTorch backend is used.
          If `be` is `None`, the backend is determined by the input arrays.
          See our :ref:`dedicated user-guide page <backend>` for more information.
  
      Returns
      -------
      float
          Kernel value
      """
      be = instantiate_backend(be, gram)
      gram = be.array(gram)
      sz1, sz2 = be.shape(gram)
  
      cum_sum = be.zeros((sz1 + 1, sz2 + 1))
      cum_sum[0, 0] = 1.0
  
      for i in range(sz1):
          for j in range(sz2):
              cum_sum[i + 1, j + 1] = (
                  cum_sum[i, j + 1] + cum_sum[i + 1, j] + cum_sum[i, j]
              ) * gram[i, j]
  
      return cum_sum[sz1, sz2]
  
imports: import numpy
  from joblib import Parallel, delayed
  from numba import njit
  from sklearn.utils import check_random_state
  from tslearn.backend import instantiate_backend
  from tslearn.utils import check_equal_size, to_time_series, to_time_series_dataset, ts_size
  from .dtw_variants import dtw, dtw_path
  from .soft_dtw_fast import _jacobian_product_sq_euc, _njit_jacobian_product_sq_euc, _njit_soft_dtw, _njit_soft_dtw_grad, _soft_dtw, _soft_dtw_grad
  from .utils import _cdist_generic
label_desc: Use image node_id 1 for calling a global function with example usage: _gak(gram, be) and returns: cum_sum