docstr: Compute distance matrix.
  
  Returns
  -------
  D: array-like, shape=(m, n)
      Distance matrix.
docstr_tok: ["Compute","distance","matrix",".","Returns","--","--","--","-","D",":","array-like",",","shape=","(","m",",","n",")","Distance","matrix","."]
code: def compute(self):
      """Compute distance matrix.
  
      Returns
      -------
      D: array-like, shape=(m, n)
          Distance matrix.
      """
      return self.be.pairwise_euclidean_distances(self.X, self.Y) ** 2
  
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 2 for calling the SquaredEuclidean obj's underlying member method code with example usage: obj.compute() and returns: unknown