code: def merge_two_slices(fgraph, slice1, len1, slice2, len2):
      """
       This function merges two slices into a single slice. The code works on
       the assumption that:
  
       a) slice1 is actually a slice and not an index, while slice2
          can be just an index.
  
       b) the two slices **have been applied consecutively** on the same
          tensor
  
      The output slice is **not** in canonical form, but actually just a slice
      that can be applied to a tensor to produce the same output as applying
      the two consecutive slices.
      ``len1`` is the length of the tensor **before** applying the first slice,
      while ``len2`` is the length **after** applying the first slice.
      """
  
      if not isinstance(slice1, slice):
          raise ValueError("slice1 should be of type `slice`")
  
      sl1, reverse1 = get_canonical_form_slice(slice1, len1)
      sl2, reverse2 = get_canonical_form_slice(slice2, len2)
  
      if not isinstance(sl2, slice):
          if reverse1 is None:
              # The first slice is not in reverse, which makes things a lot
              # more clear.
              # In this case we need to take care only of the special cases:
              # len2 <=0    -> throw index error regardless of sl2
              # sl2 > len2  -> throw index error
              # sl2 < -len2 -> throw index error
              # To get a index error we simply use len1+1 to indicate we are
              # out of bounds, because passing this index through the formula
              # of getting the mixed slice is not guaranteed to result in an
              # index error. The **issue though** if that the error will
              # complain about accessing element len1+1 which is probably not
              # too intuitive for the user
              val = sl1.start + sl2 * sl1.step
              val = switch(le(len2, 0), len1 + 1, val)
              val = switch(ge(sl2, len2), len1 + 1, val)
              val = switch(lt(sl2, 0), -len1 - 1, val)
              if sl1.step:
                  val = switch(eq(sl1.step, 0), len1 + 1, val)
              return val
          else:
              # We are in the more complex case when we do not actually know
              # if the first slice was in reverse or not.
              # in case it was not in reverse:
              p_val = sl1.start + sl2 * sl1.step
              # case it was in reverse we need to realize that we do not want
              # the k-th element from sl.start but the k-th element from
              # sl.stop backwards
              n_val = sl1.stop - 1 - sl2 * sl1.step
              # we need to pick either n_val or p_val and then follow same
              # steps as above for covering the index error cases
              val = switch(lt(reverse1, 0), n_val, p_val)
              val = switch(le(len2, 0), len1 + 1, val)
              val = switch(ge(sl2, len2), len1 + 1, val)
              val = switch(lt(sl2, 0), -len1 - 1, val)
              if sl1.step:
                  val = switch(eq(sl1.step, 0), len1 + 1, val)
              return val
      else:
          # We are deleaing with two slices that need to be put together
          # according to the two steps we have 4 different combinations of
          # positive/negative. I will denote the case I'm looking at by
          # suffixes to the variables (nn,np,pn,pp):
          flen = sl2.stop - sl2.start
          p_step = sl1.step * sl2.step
          n_step = sl1.step * sl2.step * -1
  
          pp_start = minimum(sl1.start + sl2.start * sl1.step, sl1.stop)
          pp_stop = minimum(sl1.start + sl2.stop * sl1.step, sl1.stop)
  
          pn_stop = sl1.start + (sl2.start - 1) * sl1.step
          pn_stop = switch(
              and_(lt(pn_stop, 0), gt(flen, 0)),
              -len1 - 1,
              minimum(pn_stop, sl1.stop),
          )
          pn_start = sl1.start + (sl2.stop - 1) * sl1.step
          pn_start = minimum(pn_start, sl1.stop)
          pn_start = maximum(pn_start, 0)
  
          np_stop = sl1.stop - sl2.stop * sl1.step - 1
          np_stop = switch(
              and_(lt(np_stop, 0), gt(flen, 0)),
              -len1 - 1,
              maximum(sl1.start - 1, np_stop),
          )
          np_start = maximum(
              sl1.start, sl1.stop - sl2.start * sl1.step - 1
          )
  
          nn_start = maximum(
              sl1.start, (sl1.stop - 1) - (sl2.stop - 1) * sl1.step
          )
          nn_stop = maximum(sl1.start, sl1.stop - sl2.start * sl1.step)
  
          start = switch(
              lt(reverse2 * reverse1, 0),
              switch(lt(reverse1, 0), np_start, pn_start),
              switch(lt(reverse1, 0), nn_start, pp_start),
          )
  
          stop = switch(
              lt(reverse2 * reverse1, 0),
              switch(lt(reverse1, 0), np_stop, pn_stop),
              switch(lt(reverse1, 0), nn_stop, pp_stop),
          )
  
          step = switch(lt(reverse2 * reverse1, 0), n_step, p_step)
          start = switch(le(flen, 0), 0, start)
          stop = switch(le(flen, 0), 0, stop)
  
          return slice(start, stop, step)
  
imports: import sys
  from collections.abc import Iterable
  import numpy
  import pytensor
  import pytensor.scalar.basic
  from pytensor import compile
  from pytensor.graph.basic import Constant, Variable
  from pytensor.graph.rewriting.basic import WalkingGraphRewriter, copy_stack_trace, in2out, node_rewriter
  from pytensor.raise_op import Assert
  from pytensor.tensor.basic import Alloc, Join, MakeVector, ScalarFromTensor, TensorFromScalar, alloc, as_tensor, cast, concatenate, extract_constant, get_underlying_scalar_constant_value, register_infer_shape, switch
  from pytensor.tensor.blockwise import Blockwise
  from pytensor.tensor.elemwise import Elemwise
  from pytensor.tensor.exceptions import NotScalarConstantError
  from pytensor.tensor.math import Dot, add, and_, ceil_intdiv, dot, eq, ge, gt, le, lt, maximum, minimum, or_
  from pytensor.tensor.math import all
  from pytensor.tensor.rewriting.basic import register_canonicalize, register_specialize, register_stabilize
  from pytensor.tensor.shape import Shape, SpecifyShape, Unbroadcast, shape_padleft, shape_tuple, specify_shape, unbroadcast
  from pytensor.tensor.sharedvar import TensorSharedVariable
  from pytensor.tensor.subtensor import AdvancedIncSubtensor, AdvancedIncSubtensor1, AdvancedSubtensor, AdvancedSubtensor1, IncSubtensor, Subtensor, advanced_inc_subtensor1, advanced_subtensor, advanced_subtensor1, as_index_constant, as_index_literal, get_canonical_form_slice, get_constant_idx, get_idx_list, get_slice_elements, inc_subtensor, indices_from_subtensor
  from pytensor.tensor.type import TensorType
  from pytensor.tensor.type_other import NoneTypeT, SliceConstant, SliceType
  from pytensor.tensor.variable import TensorConstant, TensorVariable
label_desc: Use image node_id 19 for calling a global function with example usage: merge_two_slices(fgraph, slice1, len1, slice2, len2) and returns: slice, val, val