code: def reverse_model(
      model: Model,
      reverse_mappings,  # TODO: type annotate reverse_mappings
      default_reverse_mapping: Callable | None = None,
      head_mapping: Callable = None,
      stop_mapping_at_tensors: list[Tensor] = None,
      verbose: bool = False,
      return_all_reversed_tensors: bool = False,
      clip_all_reversed_tensors: bool | tuple[float, float] = False,
      project_bottleneck_tensors: bool | tuple[float, float] = False,
      execution_trace: None
      | (
          tuple[
              list[Layer],
              list[tuple[Layer, list[Tensor], list[Tensor]]],
              list[Tensor],
          ]
      ) = None,
      reapply_on_copied_layers: bool = False,
  ) -> tuple[list[Tensor], dict[Tensor, ReverseTensorDict] | None]:
      """
      Reverses a Keras model based on the given reverse functions.
      Returns two values:
  
      1. the reverted tensors for the according model inputs.
      2. If `return_all_reversed_tensors` is true, a dictionary of all reversed tensors,
          otherwise None.
  
      :param model: A Keras model.
      :param reverse_mappings: Either a callable that matches layers to
        mappings or a dictionary with layers as keys and mappings as values.
        Allowed as mapping forms are:
  
            * A function of form (A) f(Xs, Ys, reversed_Ys, reverse_state).
            * A function of form f(B) f(layer, reverse_state) that returns
              a function of form (A).
            * A :class:`ReverseMappingBase` subclass.
  
      :param default_reverse_mapping: A function that reverses layers for
        which no mapping was given by param "reverse_mappings".
      :param head_mapping: Map output tensors to new values before passing
        them into the reverted network.
      :param stop_mapping_at_tensors: Tensors at which to stop the mapping.
        Similar to stop_gradient parameters for gradient computation.
      :param verbose: Print what's going on.
      :param return_all_reversed_tensors: Return all reverted tensors in addition
        to reverted model input tensors.
      :param clip_all_reversed_tensors: Clip each reverted tensor. False or tuple
        with min/max value.
      :param project_bottleneck_tensors: Project bottleneck layers in the
        reverting process into a given value range. False, True or (a, b) for
        projection range.
      :param reapply_on_copied_layers: When a model execution needs to
        linearized and copy layers before reapplying them. See
        :func:`trace_model_execution`.
      """
  
      # Set default values ######################################################
      if stop_mapping_at_tensors is None:
          stop_mapping_at_tensors = []
  
      stop_mapping_at_ids = [id(X) for X in stop_mapping_at_tensors]
  
      if head_mapping is None:
  
          def head_mapping(X):
              return X
  
      if not callable(reverse_mappings):
          # not callable, assume a dict that maps from layer to mapping
          reverse_mapping_data = reverse_mappings
  
          def reverse_mappings(layer):
              try:
                  return reverse_mapping_data[type(layer)]
              except KeyError:
                  return None
  
      if clip_all_reversed_tensors is True:
          raise NotImplementedError(
              "Keyword argument `clip_all_reversed_tensors` ",
              "expected to be `False` or tuple with min/max values.",
          )
  
      def _print(x):
          if verbose is True:
              print(x)
  
      # Initialize structure that keeps track of reversed tensors
      # maps tensor to reverse tensor and additional node information
      reversed_tensors: dict[int, ReverseTensorDict]
      reversed_tensors = {}
  
      bottleneck_tensor_ids: set[int] = set()
  
      def add_reversed_tensors(
          nid, tensors_list, reversed_tensors_list
      ) -> None:
          def add_reversed_tensor(
              i, X: Tensor, reversed_X: Tensor
          ) -> None:
              # reversed_X corresponds to the reverse-propagated relevance
              # or the output (e.g. max neuron activation)
  
              # Do not keep tensors that should stop the mapping.
              if id(X) in stop_mapping_at_ids:
                  return
  
              if reversed_X is None:
                  raise TypeError(
                      "Propagated relevance `reversed_X` is None, "
                      "is expected to be Tensor."
                  )
  
              if (
                  id(X) not in reversed_tensors
              ):  # no duplicate entries for forward tensors
                  reversed_tensors[id(X)] = {
                      "nid": (nid, i),
                      "tensors": [reversed_X],
                      "final_tensor": None,
                  }
              else:  # more than one tensor propagating relevance to X
                  reversed_tensors[id(X)]["tensors"].append(reversed_X)
  
          tmp = zip(tensors_list, reversed_tensors_list)
          for i, (X, reversed_X) in enumerate(tmp):
              add_reversed_tensor(i, X, reversed_X)
  
      def get_reversed_tensor(tensor: Tensor) -> Tensor:
          tmp: ReverseTensorDict
          tmp = reversed_tensors[id(tensor)]
  
          if tmp["final_tensor"] is None:
              if len(tmp["tensors"]) == 1:
                  final_tensor = tmp["tensors"][0]
              elif len(tmp["tensors"]) > 1:
                  final_tensor = klayers.Add()(tmp["tensors"])
              else:
                  raise RuntimeError(
                      f"Error during graph reversal: no tensors connected to {tensor}."
                  )
  
              if project_bottleneck_tensors is True:
                  if id(tensor) in bottleneck_tensor_ids:
                      project = ilayers.Project(
                          project_bottleneck_tensors
                      )
                      final_tensor = project(final_tensor)
  
              if isinstance(clip_all_reversed_tensors, tuple):
                  clip = ilayers.Clip(*clip_all_reversed_tensors)
                  final_tensor = clip(final_tensor)
  
              tmp["final_tensor"] = final_tensor
  
          return tmp["final_tensor"]
  
      # Reverse the model #######################################################
      _print(f"Reverse model: {model}")
  
      # Create a list with nodes in reverse execution order.
      if execution_trace is None:
          execution_trace = trace_model_execution(
              model, reapply_on_copied_layers=reapply_on_copied_layers
          )
      layers, execution_list, outputs = execution_trace
      reverse_execution_list = reversed(execution_list)
  
      len_execution_list = len(execution_list)
      num_input_layers = sum(
          isinstance(l, klayers.InputLayer) for l in layers
      )
      len_execution_list_wo_inputs_layers = (
          len_execution_list - num_input_layers
      )
  
      # Initialize the reverse mapping functions.
      initialized_reverse_mappings: dict[
          Layer, Callable
      ]  # TODO: specify Callable
      initialized_reverse_mappings = {}
      for layer in layers:
          # A layer can be shared, i.e., applied several times.
          # Allow to share a ReverMappingBase for each layer instance
          # in order to reduce the overhead.
  
          meta_reverse_mapping = reverse_mappings(layer)
          if meta_reverse_mapping is None:
              reverse_mapping = default_reverse_mapping
          elif inspect.isclass(meta_reverse_mapping) and issubclass(
              meta_reverse_mapping, ReverseMappingBase
          ):
              # Mapping is a class
              reverse_mapping_obj = meta_reverse_mapping(
                  layer,
                  {
                      "model": model,
                      "layer": layer,
                  },
              )
              reverse_mapping = reverse_mapping_obj.apply
          else:
  
              def parameter_count(func):
                  if hasattr(inspect, "signature"):
                      ret = len(inspect.signature(func).parameters)
                  else:
                      spec = inspect.getargspec(func)
                      ret = len(spec.args)
                      if spec.varargs is not None:
                          ret += len(spec.varargs)
                      if spec.keywords is not None:
                          ret += len(spec.keywords)
                      if ret == 3:
                          # assume class function with self
                          ret -= 1
                  return ret
  
              if (
                  callable(meta_reverse_mapping)
                  and parameter_count(meta_reverse_mapping) == 2
              ):
                  # Function that returns mapping
                  reverse_mapping = meta_reverse_mapping(
                      layer,
                      {
                          "model": model,
                          "layer": layer,
                      },
                  )
              else:
                  # Nothing meta here
                  reverse_mapping = meta_reverse_mapping
  
          # TODO: add annotations
          initialized_reverse_mappings[layer] = reverse_mapping  # type: ignore
  
      if project_bottleneck_tensors:
          ids = [
              id(X)
              for X in get_bottleneck_tensors(
                  model.inputs, outputs, execution_list
              )
          ]
          bottleneck_tensor_ids.update(ids)
  
      # Initialize the reverse tensor mappings.
      add_reversed_tensors(-1, outputs, [head_mapping(tmp) for tmp in outputs])  # type: ignore # noqa
  
      # Follow the list and revert the graph.
      for _nid, (layer, Xs, Ys) in enumerate(reverse_execution_list):
          nid = len_execution_list_wo_inputs_layers - _nid - 1
  
          if isinstance(layer, klayers.InputLayer):
              # Special case. Do nothing.
              pass
          elif ichecks.is_module(layer):
              raise Exception("This is not supposed to happen!")
          else:
              Xs, Ys = ibackend.to_list(Xs), ibackend.to_list(Ys)
              if not all(id(Y) in reversed_tensors for Y in Ys):
                  # This node is not part of our computational graph.
                  # The (node-)world is bigger than this model.
                  # Potentially this node is also not part of the
                  # reversed tensor set because it depends on a tensor
                  # that is listed in stop_mapping_at_ids.
                  continue
              reversed_Ys = [get_reversed_tensor(Y) for Y in Ys]
              local_stop_mapping_at_ids = [
                  id(X) for X in Xs if id(X) in stop_mapping_at_ids
              ]
  
              _print(f"[NID: {nid}] Reverse layer-node {layer}")
              reverse_mapping = initialized_reverse_mappings[layer]
              reversed_Xs = reverse_mapping(
                  Xs,
                  Ys,
                  reversed_Ys,
                  {
                      "nid": nid,
                      "model": model,
                      "layer": layer,
                      "stop_mapping_at_ids": local_stop_mapping_at_ids,
                  },
              )
              reversed_Xs = ibackend.to_list(reversed_Xs)
              add_reversed_tensors(nid, Xs, reversed_Xs)
  
      # Return requested values
      reversed_input_tensors = [
          get_reversed_tensor(tmp)
          for tmp in model.inputs
          if id(tmp) not in stop_mapping_at_ids
      ]
      if return_all_reversed_tensors is True:
          return reversed_input_tensors, reversed_tensors
      return reversed_input_tensors, None
  
imports: from __future__ import annotations
  import inspect
  import warnings
  from abc import ABCMeta, abstractmethod
  from typing import Any, Callable
  import numpy
  import tensorflow.keras.backend
  import tensorflow.keras.layers
  import tensorflow.keras.models
  import innvestigate.backend
  import innvestigate.backend.checks
  import innvestigate.layers
  from innvestigate.backend.types import Layer, LayerCheck, Model, NodeDict, OptionalList, ReverseTensorDict, Tensor
label_desc: Use image node_id 21 for calling a global function with example usage: reverse_model(model, reverse_mappings, default_reverse_mapping, head_mapping, stop_mapping_at_tensors, verbose, return_all_reversed_tensors, clip_all_reversed_tensors, project_bottleneck_tensors, execution_trace, reapply_on_copied_layers) and returns: reversed_input_tensors,  None, tmp, reversed_input_tensors,  reversed_tensors, X, reverse_mapping_data, None, ret