docstr: Squared Euclidean distance.
  
  Parameters
  ----------
  X: array-like, shape=(m, d)
      First time series.
  Y: array-like, shape=(n, d)
      Second time series.
  be : Backend object or string or None
      Backend.
  compute_with_backend : bool, default=False
      This parameter has no influence when the NumPy backend is used.
      When a backend different from NumPy is used (cf parameter `be`):
      If `True`, the computation is done with the corresponding backend.
      If `False`, a conversion to the NumPy backend can be used to accelerate the computation.
  
  Examples
  --------
  >>> SquaredEuclidean([1, 2, 2, 3], [1, 2, 3, 4]).compute()
  array([[0., 1., 4., 9.],
         [1., 0., 1., 4.],
         [1., 0., 1., 4.],
         [4., 1., 0., 1.]])
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code: def __init__(self, X, Y, be=None, compute_with_backend=False):
      """Squared Euclidean distance.
  
      Parameters
      ----------
      X: array-like, shape=(m, d)
          First time series.
      Y: array-like, shape=(n, d)
          Second time series.
      be : Backend object or string or None
          Backend.
      compute_with_backend : bool, default=False
          This parameter has no influence when the NumPy backend is used.
          When a backend different from NumPy is used (cf parameter `be`):
          If `True`, the computation is done with the corresponding backend.
          If `False`, a conversion to the NumPy backend can be used to accelerate the computation.
  
      Examples
      --------
      >>> SquaredEuclidean([1, 2, 2, 3], [1, 2, 3, 4]).compute()
      array([[0., 1., 4., 9.],
             [1., 0., 1., 4.],
             [1., 0., 1., 4.],
             [4., 1., 0., 1.]])
      """
      self.be = instantiate_backend(be, X, Y)
      self.compute_with_backend = compute_with_backend
      self.X = self.be.cast(
          to_time_series(X, be=be), dtype=self.be.float64
      )
      self.Y = self.be.cast(
          to_time_series(Y, be=be), dtype=self.be.float64
      )
  
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 to create a new SquaredEuclidean object  with example: obj = SquaredEuclidean(X, Y, be, compute_with_backend)