code: def get_all_layers(model, filter_fn=lambda _: True):
      """Gets all layers of a model and layers of a layer if it is a keras.Model."""
      all_layers = []
      for l in model.layers:
          if hasattr(l, "layers"):
              all_layers.extend(get_all_layers(l, filter_fn=filter_fn))
          elif filter_fn(l):
              all_layers.append(l)
      return all_layers
  
imports: import functools
  import os
  from absl import app
  from absl import flags
  from absl import logging
  import gin
  import numpy
  import reverb
  from rigl.rl.tfagents import sparse_tanh_normal_projection_network
  import tensorflow
  from tf_agents.agents import tf_agent
  from tf_agents.agents.sac import sac_agent
  from tf_agents.environments import suite_mujoco
  from tf_agents.keras_layers import inner_reshape
  from tf_agents.metrics import py_metrics
  from tf_agents.networks import nest_map
  from tf_agents.networks import sequential
  from tf_agents.policies import greedy_policy
  from tf_agents.policies import py_tf_eager_policy
  from tf_agents.policies import random_py_policy
  from tf_agents.replay_buffers import reverb_replay_buffer
  from tf_agents.replay_buffers import reverb_utils
  from tf_agents.train import actor
  from tf_agents.train import learner
  from tf_agents.train import triggers
  from tf_agents.train.utils import spec_utils
  from tf_agents.train.utils import strategy_utils
  from tf_agents.train.utils import train_utils
  from tf_agents.utils import common
  from tf_agents.utils import object_identity
label_desc: Use image node_id 2 for calling a global function with example usage: get_all_layers(model, filter_fn) and returns: all_layers