code: def __init__(
      self,
      name=None,
      num_actions=2,
      stateful=True,
      use_constant_initializer=True,
  ):
      if stateful:
          state_spec = tensor_spec.TensorSpec(
              shape=(1,), dtype=tf.float32
          )
      else:
          state_spec = ()
      super(DummyNet, self).__init__(
          input_tensor_spec=tensor_spec.TensorSpec(
              [2], tf.float32, "obs"
          ),
          state_spec=state_spec,
          name=name,
      )
  
      kernel_initializer = None
      bias_initializer = None
      if use_constant_initializer:
          kernel_initializer = tf.constant_initializer(
              [[1, 200], [3, 4]]
          )
          bias_initializer = tf.constant_initializer([1, 1])
  
      # Store custom layers that can be serialized through the Checkpointable API.
      self._dummy_layers = [
          tf.keras.layers.Dense(
              num_actions,
              kernel_initializer=kernel_initializer,
              bias_initializer=bias_initializer,
          )
      ]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl import flags
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.networks import network
  from tf_agents.policies import actor_policy
  from tf_agents.policies import ou_noise_policy
  from tf_agents.policies import py_tf_policy
  from tf_agents.policies import q_policy
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
label_desc: Use image node_id 1 to create a new DummyNet object from inherited base classes: network with example: obj = DummyNet(name, num_actions, stateful, use_constant_initializer)