code: def _local_squared_dist(x, y, be=None):
      """Compute the squared distance between two vectors.
  
      Parameters
      ----------
      x : array-like, shape=(d,)
          A vector.
      y : array-like, shape=(d,)
          Another vector.
      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
      -------
      dist : float
          Squared distance between x and y.
      """
      be = instantiate_backend(be, x, y)
      x = be.array(x)
      y = be.array(y)
      dist = 0.0
      for di in range(be.shape(x)[0]):
          diff = x[di] - y[di]
          dist += diff * diff
      return dist
  
imports: import warnings
  import numpy
  from numba import njit, prange
  from tslearn.backend import instantiate_backend
  from tslearn.utils import to_time_series
  from .utils import _cdist_generic
label_desc: Use image node_id 2 for calling a global function with example usage: _local_squared_dist(x, y, be) and returns: dist