code: def get_dummy_net(action_spec, observation_spec=None):
      flat_action_spec = tf.nest.flatten(action_spec)[0]
      if flat_action_spec.dtype.is_integer:
          # Emitting discrete actions.
          num_actions = (
              flat_action_spec.maximum - flat_action_spec.minimum + 1
          )
          kernel_initializer = tf.constant_initializer([[2, 1], [1, 1]])
          bias_initializer = tf.constant_initializer([[1], [1]])
          final_shape = [num_actions]
      else:
          # Emitting continuous vectors.
          num_actions = np.prod(flat_action_spec.shape)
          kernel_initializer = None
          bias_initializer = None
          final_shape = flat_action_spec.shape
  
      return sequential.Sequential(
          [
              tf.keras.layers.Dense(
                  num_actions,
                  kernel_initializer=kernel_initializer,
                  bias_initializer=bias_initializer,
                  dtype=tf.float32,
              ),
              inner_reshape.InnerReshape([None], final_shape),
          ],
          input_spec=observation_spec,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents import test_util
  from tf_agents.agents.behavioral_cloning import behavioral_cloning_agent
  from tf_agents.keras_layers import inner_reshape
  from tf_agents.networks import expand_dims_layer
  from tf_agents.networks import q_network
  from tf_agents.networks import q_rnn_network
  from tf_agents.networks import sequential
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import test_utils
label_desc: Use image node_id 2 for calling a global function with example usage: get_dummy_net(action_spec, observation_spec) and returns: sequential