code: def _njit_itakura_mask(sz1, sz2, max_slope=2.0):
      """Compute the Itakura mask without checking that the constraints
      are feasible. In most cases, you should use itakura_mask instead.
  
      Parameters
      ----------
      sz1 : int
          The size of the first time series
  
      sz2 : int
          The size of the second time series.
  
      max_slope : float (default = 2)
          The maximum slope of the parallelogram.
  
      Returns
      -------
      mask : array-like, shape=(sz1, sz2)
          Itakura mask.
      """
      min_slope = 1 / float(max_slope)
      max_slope *= float(sz1) / float(sz2)
      min_slope *= float(sz1) / float(sz2)
  
      lower_bound = numpy.empty((2, sz2))
      lower_bound[0] = min_slope * numpy.arange(sz2)
      lower_bound[1] = (
          (sz1 - 1)
          - max_slope * (sz2 - 1)
          + max_slope * numpy.arange(sz2)
      )
      lower_bound_ = numpy.empty(sz2)
      for i in prange(sz2):
          lower_bound_[i] = max(
              round(lower_bound[0, i], 2), round(lower_bound[1, i], 2)
          )
      lower_bound_ = numpy.ceil(lower_bound_)
  
      upper_bound = numpy.empty((2, sz2))
      upper_bound[0] = max_slope * numpy.arange(sz2)
      upper_bound[1] = (
          (sz1 - 1)
          - min_slope * (sz2 - 1)
          + min_slope * numpy.arange(sz2)
      )
      upper_bound_ = numpy.empty(sz2)
      for i in prange(sz2):
          upper_bound_[i] = min(
              round(upper_bound[0, i], 2), round(upper_bound[1, i], 2)
          )
      upper_bound_ = numpy.floor(upper_bound_ + 1)
  
      mask = numpy.full((sz1, sz2), numpy.inf)
      for i in prange(sz2):
          mask[int(lower_bound_[i]) : int(upper_bound_[i]), i] = 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 28 for calling a global function with example usage: _njit_itakura_mask(sz1, sz2, max_slope) and returns: mask