old_contents: import numpy as np
  from numba import njit, types
  from numba.extending import overload, register_jitable
  from numba.errors import TypingError
  
  import scipy.linalg
  
  
  @register_jitable
  def _oneD_norm_2(a):
      val = np.abs(a)
      return np.sqrt(np.sum(val * val))
  
  
  @overload(scipy.linalg.norm)
  def jit_norm(a, ord=None):
      if not isinstance(a, types.Array):
          raise TypingError("Only accepts NumPy ndarray")
      if not isinstance(a.dtype, (types.Integer, types.Float)):
          raise TypingError("Only integer and floating point types accpeted")
      if a.ndim not in [1, 2]:
          raise TypingError('3D and beyond are not allowed')
      elif a.ndim == 1:
          def _oneD_norm_x(a, ord=None):
              if ord == 2 or ord is None:
                  return _oneD_norm_2(a)
              elif ord == np.inf:
                  return np.max(np.abs(a))
              elif ord == -np.inf:
                  return np.min(np.abs(a))
              elif ord == 0:
                  return np.sum(a != 0)
              elif ord == 1:
                  return np.sum(np.abs(a))
              else:
                  return np.sum(np.abs(a)**ord)**(1. / ord)
          return _oneD_norm_x
      elif a.ndim == 2:
          def _two_D_norm_2(a, ord=None):
              return _oneD_norm_2(a.ravel())
          return _two_D_norm_2
  
  
  @njit
  def use(a, ord=None):
      return scipy.linalg.norm(a, ord)
  
  
  if __name__ == "__main__":
      a = np.arange(10)
      print(use(a))
      print(scipy.linalg.norm(a))
      b = np.arange(9).reshape((3, 3))
      print(use(b))
      print(scipy.linalg.norm(b))
  
new_contents: import numpy as np
  from numba import njit, types
  from numba.extending import overload, register_jitable
  from numba.errors import TypingError
  
  import scipy.linalg
  
  
  @register_jitable
  def _oneD_norm_2(a):
      # re-usable implementation of the 2-norm
      val = np.abs(a)
      return np.sqrt(np.sum(val * val))
  
  
  @overload(scipy.linalg.norm)
  def jit_norm(a, ord=None):
      # Reject non-ndarray types
      if not isinstance(a, types.Array):
          raise TypingError("Only accepts NumPy ndarray")
      # Reject ndarrays with non integer or floating-point types
      if not isinstance(a.dtype, (types.Integer, types.Float)):
          raise TypingError("Only integer and floating point types accpeted")
      # Reject ndarrays with unsupported dimensionality
      if a.ndim not in [1, 2]:
          raise TypingError('3D and beyond are not allowed')
      # Implementation for vectors
      elif a.ndim == 1:
          def _oneD_norm_x(a, ord=None):
              if ord == 2 or ord is None:
                  return _oneD_norm_2(a)
              elif ord == np.inf:
                  return np.max(np.abs(a))
              elif ord == -np.inf:
                  return np.min(np.abs(a))
              elif ord == 0:
                  return np.sum(a != 0)
              elif ord == 1:
                  return np.sum(np.abs(a))
              else:
                  return np.sum(np.abs(a)**ord)**(1. / ord)
          return _oneD_norm_x
      # Implementation for matrices
      elif a.ndim == 2:
          def _two_D_norm_2(a, ord=None):
              return _oneD_norm_2(a.ravel())
          return _two_D_norm_2
  
  
  if __name__ == "__main__":
      @njit
      def use(a, ord=None):
          # simple test function to check that the overload works
          return scipy.linalg.norm(a, ord)
  
      # spot check for vectors
      a = np.arange(10)
      print(use(a))
      print(scipy.linalg.norm(a))
  
      # spot check for matrices
      b = np.arange(9).reshape((3, 3))
      print(use(b))
      print(scipy.linalg.norm(b))
  
subject: improve the code example