code: def model_contains(
      model: Model,
      layer_condition: OptionalList[LayerCheck],
  ) -> list[list[Layer]]:
      """
      Collect layers in model which satisfy `layer_condition`.
      If multiple conditions are given in `layer_condition`,
      the collected layers are returned for each condition.
  
      :param model: A Keras model.
      :type model: Model
      :param layer_condition: A boolean function or list of functions that
          check Keras layers.
      :type layer_condition: Union[LayerCheck, List[LayerCheck]]
      :return: List, which for each condition in layer_condition
          contains a list of layers which satisfy that condition.
      :rtype: List[List[Layer]]
      """
      conditions = ibackend.to_list(layer_condition)
      layers = get_model_layers(model)
  
      # return layers for which condition c holds true
      return [[l for l in layers if c(l)] for c in conditions]
  
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 13 for calling a global function with example usage: model_contains(model, layer_condition) and returns: unknown