code: def test_cases():
      return parameterized.named_parameters(
          {
              "testcase_name": "_batch1_contextdim10_float32",
              "batch_size": 1,
              "context_dim": 10,
              "exploration_policy": (
                  linear_agent.ExplorationPolicy.linear_ucb_policy
              ),
              "dtype": tf.float32,
          },
          {
              "testcase_name": "_batch4_contextdim5_float64_UCB",
              "batch_size": 4,
              "context_dim": 5,
              "exploration_policy": (
                  linear_agent.ExplorationPolicy.linear_ucb_policy
              ),
              "dtype": tf.float64,
          },
          {
              "testcase_name": "_batch4_contextdim5_float64_TS",
              "batch_size": 4,
              "context_dim": 5,
              "exploration_policy": (
                  linear_agent.ExplorationPolicy.linear_thompson_sampling_policy
              ),
              "dtype": tf.float64,
          },
          {
              "testcase_name": "_batch4_contextdim5_float64_decomp",
              "batch_size": 4,
              "context_dim": 5,
              "exploration_policy": (
                  linear_agent.ExplorationPolicy.linear_ucb_policy
              ),
              "dtype": tf.float64,
              "use_eigendecomp": True,
          },
          {
              "testcase_name": "_batch1_contextdim10_float32_with_weights",
              "batch_size": 1,
              "context_dim": 10,
              "exploration_policy": (
                  linear_agent.ExplorationPolicy.linear_ucb_policy
              ),
              "dtype": tf.float32,
              "set_example_weights": True,
          },
          {
              "testcase_name": "_batch4_contextdim5_float64_UCB_with_weights",
              "batch_size": 4,
              "context_dim": 5,
              "exploration_policy": (
                  linear_agent.ExplorationPolicy.linear_ucb_policy
              ),
              "dtype": tf.float64,
              "set_example_weights": True,
          },
          {
              "testcase_name": "_batch4_contextdim5_float64_TS_with_weights",
              "batch_size": 4,
              "context_dim": 5,
              "exploration_policy": (
                  linear_agent.ExplorationPolicy.linear_thompson_sampling_policy
              ),
              "dtype": tf.float64,
              "set_example_weights": True,
          },
          {
              "testcase_name": "_batch4_contextdim5_float64_decomp_with_weights",
              "batch_size": 4,
              "context_dim": 5,
              "exploration_policy": (
                  linear_agent.ExplorationPolicy.linear_ucb_policy
              ),
              "dtype": tf.float64,
              "use_eigendecomp": True,
              "set_example_weights": True,
          },
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from typing import Optional, Tuple
  from absl import flags
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.bandits.agents import lin_ucb_agent
  from tf_agents.bandits.agents import linear_bandit_agent
  from tf_agents.bandits.agents import linear_thompson_sampling_agent
  from tf_agents.bandits.agents import utils
  from tf_agents.bandits.drivers import driver_utils
  from tf_agents.bandits.specs import utils
  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.typing import types
  from tf_agents.utils import common
label_desc: Use image node_id 1 for calling a global function with example usage: test_cases() and returns: parameterized