code: def get_model_execution_trace(
      model: Model,
      keep_input_layers: bool = False,
      reapply_on_copied_layers: bool = False,
  ) -> list[NodeDict]:
      """
      Returns a list representing the execution graph.
      Each key is the node's id as it is used by the reverse_model method.
  
      Each associated value contains a dictionary with the following items:
  
      * `nid`: the node id.
      * `layer`: the layer creating this node.
      * `Xs`: the input tensors (only valid if not in a nested container).
      * `Ys`: the output tensors (only valid if not in a nested container).
      * `Xs_nids`: the ids of the nodes creating the Xs.
      * `Ys_nids`: the ids of nodes using the according output tensor.
      * `Xs_layers`: the layer that created the according input tensor.
      * `Ys_layers`: the layers using the according output tensor.
  
      :param model: A kera model.
      :param keep_input_layers: Keep input layers.
      :param reapply_on_copied_layers: If the execution needs to be linearized,
        reapply with copied layers. Might be slow. Prevents changes of the
        original layer's node lists.
      """
      # Get execution_trace: list with a linearized execution
      # consisting of `(layer, input_tensors, output_tensors)`.
      execution_trace: list[tuple[Layer, list[Tensor], list[Tensor]]]
      _, execution_trace, _ = trace_model_execution(
          model, reapply_on_copied_layers=reapply_on_copied_layers
      )
  
      # Enrich execution_trace with node ids to get id_execution_trace
      nid: int | None
      current_nid: int
      id_execution_trace: list[
          tuple[int | None, Layer, list[Tensor], list[Tensor]]
      ]
  
      current_nid = 0
      id_execution_trace = []
      for l, Xs, Ys in execution_trace:
          if isinstance(l, klayers.InputLayer):
              id_execution_trace.append((None, l, Xs, Ys))
          else:
              id_execution_trace.append((current_nid, l, Xs, Ys))
              current_nid += 1
  
      # Create lookups from tensor to creating or receiving layer-node
      inputs_to_node: dict[int, list[int]]
      outputs_to_node: dict[int, int | None]
  
      inputs_to_node = {}
      outputs_to_node = {}
      for nid, _l, Xs, Ys in id_execution_trace:
          if nid is not None:
              for X in Xs:
                  Xid = id(X)
                  if Xid in inputs_to_node:
                      inputs_to_node[Xid].append(nid)
                  else:
                      inputs_to_node[Xid] = [nid]
  
          if keep_input_layers or nid is not None:
              for Y in Ys:
                  Yid = id(Y)
                  if Yid in inputs_to_node:
                      raise Exception(
                          "Cannot be more than one creating node."
                      )
                  outputs_to_node[Yid] = nid
  
      # Enrich trace with this info.
      nid_to_nodes: dict[
          Layer, tuple[int | None, Layer, list[Tensor], list[Tensor]]
      ]
      model_execution_trace: list[NodeDict]
  
      # TODO: fix invariance of type hints
      Xs_nids: list[int | None]
      Ys_nids: list[list[int] | list[None]]
      # Xs_layers = List[Layer]
      # Ys_layers = List[List[Layer]]
  
      nid_to_nodes = {t[0]: t for t in id_execution_trace}
  
      model_execution_trace = []
      for nid, l, Xs, Ys in id_execution_trace:
          if isinstance(l, klayers.InputLayer):
              # The nids that created or receive the tensors.
              Xs_nids = []  # Input layer does not receive.
              Ys_nids = [inputs_to_node[id(Y)] for Y in Ys]
              # The layers that created or receive the tensors.
              Xs_layers = []  # Input layer does not receive.
              Ys_layers = [
                  [nid_to_nodes[Ynid][1] for Ynid in Ynids2]
                  for Ynids2 in Ys_nids
              ]
          else:
              # The nids that created or receive the tensors.
              Xs_nids = [outputs_to_node.get(id(X), None) for X in Xs]
              Ys_nids = [inputs_to_node.get(id(Y), [None]) for Y in Ys]
              # The layers that created or receive the tensors.
              Xs_layers = [
                  nid_to_nodes[Xnid][1]
                  for Xnid in Xs_nids
                  if Xnid is not None
              ]
              Ys_layers = [
                  [
                      nid_to_nodes[Ynid][1]
                      for Ynid in Ynids2
                      if Ynid is not None
                  ]
                  for Ynids2 in Ys_nids
              ]
  
          entry: NodeDict = {
              "nid": nid,
              "layer": l,
              "Xs": Xs,
              "Ys": Ys,
              "Xs_nids": Xs_nids,
              "Ys_nids": Ys_nids,
              "Xs_layers": Xs_layers,
              "Ys_layers": Ys_layers,
          }
          model_execution_trace.append(entry)
  
      if not keep_input_layers:
          model_execution_trace = [
              t for t in model_execution_trace if t["nid"] is not None
          ]
  
      return model_execution_trace
  
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 16 for calling a global function with example usage: get_model_execution_trace(model, keep_input_layers, reapply_on_copied_layers) and returns: model_execution_trace