code: def testInitializeAgentNumTrainSteps0(self, batch_size, context_dim):
      num_actions = 5
      observation_spec = tensor_spec.TensorSpec(
          [context_dim], tf.float32
      )
      time_step_spec = time_step.time_step_spec(observation_spec)
      action_spec = tensor_spec.BoundedTensorSpec(
          dtype=tf.int32, shape=(), minimum=0, maximum=num_actions - 1
      )
  
      encoder = DummyNet(observation_spec)
      agent = neural_linucb_agent.NeuralLinUCBAgent(
          time_step_spec=time_step_spec,
          action_spec=action_spec,
          encoding_network=encoder,
          encoding_network_num_train_steps=0,
          encoding_dim=10,
          optimizer=None,
      )
      self.evaluate(agent.initialize())
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.bandits.agents import neural_linucb_agent
  from tf_agents.bandits.agents import utils
  from tf_agents.bandits.drivers import driver_utils
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.bandits.specs import utils
  from tf_agents.networks import network
  from tf_agents.policies import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.utils import common
label_desc: Use image node_id 2 for calling the NeuralLinUCBAgentTest obj's underlying member method code with example usage: obj.testInitializeAgentNumTrainSteps0(batch_size, context_dim) without return types