code: def __init__(self, name=None, num_actions=3):
      input_spec = array_spec.ArraySpec([2], np.float32)
      action_spec = array_spec.BoundedArraySpec(
          [1], np.float32, 1, num_actions
      )
  
      input_tensor_spec = tensor_spec.from_spec(input_spec)
      action_tensor_spec = tensor_spec.from_spec(action_spec)
  
      super(HeteroscedasticDummyNet, self).__init__(
          input_tensor_spec, action_tensor_spec
      )
      self._value_layer = tf.keras.layers.Dense(
          num_actions,
          kernel_initializer=tf.constant_initializer(
              [[1, 1.5, 2], [1, 1.5, 4]]
          ),
          bias_initializer=tf.constant_initializer([[1], [1], [-10]]),
      )
  
      self._log_variance_layer = tf.keras.layers.Dense(
          num_actions,
          kernel_initializer=tf.constant_initializer(
              [[1, 1.5, 2], [1, 1.5, 4]]
          ),
          bias_initializer=tf.constant_initializer([[1], [1], [-10]]),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.bandits.networks import heteroscedastic_q_network
  from tf_agents.bandits.policies import boltzmann_reward_prediction_policy
  from tf_agents.bandits.policies import constraints
  from tf_agents.bandits.policies import falcon_reward_prediction_policy
  from tf_agents.bandits.policies import greedy_reward_prediction_policy
  from tf_agents.bandits.specs import utils
  from tf_agents.networks import network
  from tf_agents.specs import array_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 1 to create a new HeteroscedasticDummyNet object from inherited base classes: heteroscedastic_q_network with example: obj = HeteroscedasticDummyNet(name, num_actions)