code: def compute_mask(
      s1,
      s2,
      global_constraint=0,
      sakoe_chiba_radius=None,
      itakura_max_slope=None,
      be=None,
  ):
      """Compute the mask (region constraint).
  
      Parameters
      ----------
      s1 : array-like, shape=(sz1, d) or (sz1,)
          A time series or integer.
          If shape is (sz1,), the time series is assumed to be univariate.
      s2 : array-like, shape=(sz2, d) or (sz2,)
          Another time series or integer.
          If shape is (sz2,), the time series is assumed to be univariate.
      global_constraint : {0, 1, 2} (default: 0)
          Global constraint to restrict admissible paths for DTW:
          - "itakura" if 1
          - "sakoe_chiba" if 2
          - no constraint otherwise
      sakoe_chiba_radius : int or None (default: None)
          Radius to be used for Sakoe-Chiba band global constraint.
          If None and `global_constraint` is set to 2 (sakoe-chiba), a radius of
          1 is used.
          If both `sakoe_chiba_radius` and `itakura_max_slope` are set,
          `global_constraint` is used to infer which constraint to use among the
          two. In this case, if `global_constraint` corresponds to no global
          constraint, a `RuntimeWarning` is raised and no global constraint is
          used.
      itakura_max_slope : float or None (default: None)
          Maximum slope for the Itakura parallelogram constraint.
          If None and `global_constraint` is set to 1 (itakura), a maximum slope
          of 2. is used.
          If both `sakoe_chiba_radius` and `itakura_max_slope` are set,
          `global_constraint` is used to infer which constraint to use among the
          two. In this case, if `global_constraint` corresponds to no global
          constraint, a `RuntimeWarning` is raised and no global constraint is
          used.
      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
      -------
      mask : array-like, shape=(sz1, sz2)
          Constraint region.
      """
      be = instantiate_backend(be, s1, s2)
      # The output mask will be of shape (sz1, sz2)
      if isinstance(s1, int) and isinstance(s2, int):
          sz1, sz2 = s1, s2
      else:
          s1 = be.array(s1)
          s2 = be.array(s2)
          sz1 = be.shape(s1)[0]
          sz2 = be.shape(s2)[0]
      if (
          global_constraint == 0
          and sakoe_chiba_radius is not None
          and itakura_max_slope is not None
      ):
          raise RuntimeWarning(
              "global_constraint is not set for DTW, but both "
              "sakoe_chiba_radius and itakura_max_slope are "
              "set, hence global_constraint cannot be inferred "
              "and no global constraint will be used."
          )
      if global_constraint == 2 or (
          global_constraint == 0 and sakoe_chiba_radius is not None
      ):
          if sakoe_chiba_radius is None:
              sakoe_chiba_radius = 1
          if be.is_numpy:
              mask = njit_sakoe_chiba_mask(
                  sz1, sz2, radius=sakoe_chiba_radius
              )
          else:
              mask = sakoe_chiba_mask(
                  sz1, sz2, radius=sakoe_chiba_radius, be=be
              )
      elif global_constraint == 1 or (
          global_constraint == 0 and itakura_max_slope is not None
      ):
          if itakura_max_slope is None:
              itakura_max_slope = 2.0
          mask = itakura_mask(
              sz1, sz2, max_slope=itakura_max_slope, be=be
          )
      else:
          mask = be.zeros((sz1, sz2))
      return mask
  
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 31 for calling a global function with example usage: compute_mask(s1, s2, global_constraint, sakoe_chiba_radius, itakura_max_slope, be) and returns: mask