code: def njit_sakoe_chiba_mask(sz1, sz2, radius=1):
      """Compute the Sakoe-Chiba mask.
  
      Parameters
      ----------
      sz1 : int
          The size of the first time series
      sz2 : int
          The size of the second time series.
      radius : int
          The radius of the band.
  
      Returns
      -------
      mask : array-like, shape=(sz1, sz2)
          Sakoe-Chiba mask.
  
      Examples
      --------
      >>> njit_sakoe_chiba_mask(4, 4, 1)
      array([[ 0.,  0., inf, inf],
             [ 0.,  0.,  0., inf],
             [inf,  0.,  0.,  0.],
             [inf, inf,  0.,  0.]])
      >>> njit_sakoe_chiba_mask(7, 3, 1)
      array([[ 0.,  0., inf],
             [ 0.,  0.,  0.],
             [ 0.,  0.,  0.],
             [ 0.,  0.,  0.],
             [ 0.,  0.,  0.],
             [ 0.,  0.,  0.],
             [inf,  0.,  0.]])
      """
      mask = numpy.full((sz1, sz2), numpy.inf)
      if sz1 > sz2:
          width = sz1 - sz2 + radius
          for i in prange(sz2):
              lower = max(0, i - radius)
              upper = min(sz1, i + width) + 1
              mask[lower:upper, i] = 0.0
      else:
          width = sz2 - sz1 + radius
          for i in prange(sz1):
              lower = max(0, i - radius)
              upper = min(sz2, i + width) + 1
              mask[i, lower:upper] = 0.0
      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 26 for calling a global function with example usage: njit_sakoe_chiba_mask(sz1, sz2, radius) and returns: mask