docstr: A simple linear network.
  
  Args:
    observation_spec: The observation specification.
    kernel_weights: A 2-d numpy array of shape [input_size, output_size].
    bias: A 1-d numpy array of shape [output_size].
code: def __init__(
      self,
      observation_spec: types.NestedTensorSpec,
      kernel_weights: np.ndarray,
      bias: np.ndarray,
  ):
      """A simple linear network.
  
      Args:
        observation_spec: The observation specification.
        kernel_weights: A 2-d numpy array of shape [input_size, output_size].
        bias: A 1-d numpy array of shape [output_size].
      """
      super(DummyNet, self).__init__(observation_spec, (), "DummyNet")
      assert len(kernel_weights.shape) == 2
      assert len(bias.shape) == 1
      assert kernel_weights.shape[1] == bias.shape[0]
  
      # Store custom layers that can be serialized through the Checkpointable API.
      self._dummy_layers = [
          tf.keras.layers.Dense(
              kernel_weights.shape[1],
              kernel_initializer=tf.constant_initializer(
                  kernel_weights
              ),
              bias_initializer=tf.constant_initializer(bias),
          )
      ]
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from typing import Dict, List, Text, Tuple
  import numpy
  import tensorflow
  from tf_agents.bandits.multi_objective import multi_objective_scalarizer
  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 greedy_multi_objective_neural_policy
  from tf_agents.bandits.specs import utils
  from tf_agents.networks import network
  from tf_agents.policies import utils
  from tf_agents.specs import array_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  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(observation_spec, kernel_weights, bias)