code: def get_model_execution_graph(
      model: Model, keep_input_layers: bool = False
  ) -> dict[int | None, NodeDict | list[NodeDict]]:
      """
      Returns a dictionary 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.
      """
      trace: list[NodeDict]
      input_layers: list[NodeDict]
      graph: dict[int | None, NodeDict | list[NodeDict]]
  
      trace = get_model_execution_trace(
          model,
          keep_input_layers=keep_input_layers,
          reapply_on_copied_layers=False,
      )
  
      # Input layers in graph have Node-ID `nid=None`.
      # Extract these from list of all nodes `trace`.
      input_layers = [node for node in trace if node["nid"] is None]
  
      # Create graph which maps Node-IDs to node information from full trace.
      graph = {node["nid"]: node for node in trace}
      if keep_input_layers:
          graph[None] = input_layers
  
      return graph
  
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 17 for calling a global function with example usage: get_model_execution_graph(model, keep_input_layers) and returns: graph