code: def numba_funcify_DimShuffle(op, node, **kwargs):
      shuffle = tuple(op.shuffle)
      transposition = tuple(op.transposition)
      augment = tuple(op.augment)
      inplace = op.inplace
  
      ndim_new_shape = len(shuffle) + len(augment)
  
      no_transpose = all(i == j for i, j in enumerate(transposition))
      if no_transpose:
  
          @numba_basic.numba_njit
          def transpose(x):
              return x
  
      else:
  
          @numba_basic.numba_njit
          def transpose(x):
              return np.transpose(x, transposition)
  
      shape_template = (1,) * ndim_new_shape
  
      # When `len(shuffle) == 0`, the `shuffle_shape[j]` expression below
      # is typed as `getitem(Tuple(), int)`, which has no implementation
      # (since getting an item from an empty sequence doesn't make sense).
      # To avoid this compile-time error, we omit the expression altogether.
      if len(shuffle) > 0:
          # Use the statically known shape if available
          if all(
              length is not None
              for length in node.outputs[0].type.shape
          ):
              shape = node.outputs[0].type.shape
  
              @numba_basic.numba_njit
              def find_shape(array_shape):
                  return shape
  
          else:
  
              @numba_basic.numba_njit
              def find_shape(array_shape):
                  shape = shape_template
                  j = 0
                  for i in range(ndim_new_shape):
                      if i not in augment:
                          length = array_shape[j]
                          shape = numba_basic.tuple_setitem(
                              shape, i, length
                          )
                          j = j + 1
                  return shape
  
      else:
  
          @numba_basic.numba_njit
          def find_shape(array_shape):
              return shape_template
  
      if ndim_new_shape > 0:
  
          @numba_basic.numba_njit
          def dimshuffle_inner(x, shuffle):
              x = transpose(x)
              shuffle_shape = x.shape[: len(shuffle)]
              new_shape = find_shape(shuffle_shape)
  
              # FIXME: Numba's `array.reshape` only accepts C arrays.
              res_reshape = np.reshape(
                  np.ascontiguousarray(x), new_shape
              )
  
              if not inplace:
                  return res_reshape.copy()
              else:
                  return res_reshape
  
      else:
  
          @numba_basic.numba_njit
          def dimshuffle_inner(x, shuffle):
              return np.reshape(np.ascontiguousarray(x), ())
  
      # Without the following wrapper function we would see this error:
      # E   No implementation of function Function(<built-in function getitem>) found for signature:
      # E
      # E    >>> getitem(UniTuple(int64 x 2), slice<a:b>)
      # E
      # E   There are 22 candidate implementations:
      # E      - Of which 22 did not match due to:
      # E      Overload of function 'getitem': File: <numerous>: Line N/A.
      # E        With argument(s): '(UniTuple(int64 x 2), slice<a:b>)':
      # E       No match.
      # ...(on this line)...
      # E           shuffle_shape = res.shape[: len(shuffle)]
      @numba_basic.numba_njit(inline="always")
      def dimshuffle(x):
          return dimshuffle_inner(np.asarray(x), shuffle)
  
      return dimshuffle
  
imports: import base64
  import pickle
  from functools import singledispatch
  from numbers import Number
  from textwrap import indent
  from typing import Any, Callable, Optional, Union
  import numba
  import numpy
  from numba import TypingError, types
  from numba.core import cgutils
  from numba.core.extending import overload
  from numba.np import arrayobj
  from numpy.core.numeric import normalize_axis_index, normalize_axis_tuple
  from pytensor import config
  from pytensor.graph.basic import Apply
  from pytensor.graph.op import Op
  from pytensor.link.numba.dispatch import basic
  from pytensor.link.numba.dispatch import elemwise_codegen
  from pytensor.link.numba.dispatch.basic import create_numba_signature, create_tuple_creator, numba_funcify, numba_njit, use_optimized_cheap_pass
  from pytensor.link.utils import compile_function_src, get_name_for_object
  from pytensor.scalar.basic import AND, OR, XOR, Add, Composite, IntDiv, Mean, Mul, ScalarMaximum, ScalarMinimum, Sub, TrueDiv, scalar_maximum
  from pytensor.scalar.basic import add
  from pytensor.tensor.elemwise import CAReduce, DimShuffle, Elemwise
  from pytensor.tensor.math import MaxAndArgmax, MulWithoutZeros, Sum
  from pytensor.tensor.special import LogSoftmax, Softmax, SoftmaxGrad
  from pytensor.tensor.type import scalar
label_desc: Use image node_id 23 for calling a global function with example usage: numba_funcify_DimShuffle(op, node) and returns: dimshuffle, dimshuffle_inner, x, np, shape_template, np, shape, shape, res_reshape, res_reshape