code: def numba_funcify_RavelMultiIndex(op, node, **kwargs):
      mode = op.mode
      order = op.order
  
      if order != "C":
          raise NotImplementedError(
              "Numba does not implement `order` in `numpy.ravel_multi_index`"
          )
  
      if mode == "raise":
  
          @numba_basic.numba_njit
          def mode_fn(*args):
              raise ValueError("invalid entry in coordinates array")
  
      elif mode == "wrap":
  
          @numba_basic.numba_njit(inline="always")
          def mode_fn(new_arr, i, j, v, d):
              new_arr[i, j] = v % d
  
      elif mode == "clip":
  
          @numba_basic.numba_njit(inline="always")
          def mode_fn(new_arr, i, j, v, d):
              new_arr[i, j] = min(max(v, 0), d - 1)
  
      if node.inputs[0].ndim == 0:
  
          @numba_basic.numba_njit
          def ravelmultiindex(*inp):
              shape = inp[-1]
              arr = np.stack(inp[:-1])
  
              new_arr = arr.T.astype(np.float64).copy()
              for i, b in enumerate(new_arr):
                  if b < 0 or b >= shape[i]:
                      mode_fn(new_arr, i, 0, b, shape[i])
  
              a = np.ones(len(shape), dtype=np.float64)
              a[: len(shape) - 1] = np.cumprod(shape[-1:0:-1])[::-1]
              return np.array(a.dot(new_arr.T), dtype=np.int64)
  
      else:
  
          @numba_basic.numba_njit
          def ravelmultiindex(*inp):
              shape = inp[-1]
              arr = np.stack(inp[:-1])
  
              new_arr = arr.T.astype(np.float64).copy()
              for i, b in enumerate(new_arr):
                  for j, (d, v) in enumerate(zip(shape, b)):
                      if v < 0 or v >= d:
                          mode_fn(new_arr, i, j, v, d)
  
              a = np.ones(len(shape), dtype=np.float64)
              a[: len(shape) - 1] = np.cumprod(shape[-1:0:-1])[::-1]
              return a.dot(new_arr.T).astype(np.int64)
  
      return ravelmultiindex
  
imports: import warnings
  from typing import cast
  import numba
  import numpy
  from pytensor import config
  from pytensor.graph import Apply
  from pytensor.link.numba.dispatch import basic
  from pytensor.link.numba.dispatch.basic import get_numba_type, numba_funcify
  from pytensor.raise_op import CheckAndRaise
  from pytensor.tensor import TensorVariable
  from pytensor.tensor.extra_ops import Bartlett, CumOp, FillDiagonal, FillDiagonalOffset, RavelMultiIndex, Repeat, SearchsortedOp, Unique, UnravelIndex
label_desc: Use image node_id 5 for calling a global function with example usage: numba_funcify_RavelMultiIndex(op, node) and returns: ravelmultiindex, np, a