code: def _time_step_batch_with_mask(self, batch_size):
      no_mask_observation = tf.constant(
          np.array(range(batch_size * self._obs_dim)),
          dtype=tf.float32,
          shape=[batch_size, self._obs_dim],
      )
      mask = tf.eye(
          batch_size, num_columns=self._num_actions, dtype=tf.int32
      )
      observation = (no_mask_observation, mask)
      return ts.TimeStep(
          tf.constant(
              ts.StepType.FIRST,
              dtype=tf.int32,
              shape=[batch_size],
              name="step_type",
          ),
          tf.constant(
              0.0, dtype=tf.float32, shape=[batch_size], name="reward"
          ),
          tf.constant(
              1.0, dtype=tf.float32, shape=[batch_size], name="discount"
          ),
          observation,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.policies import linear_bandit_policy
  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 time_step
  from tf_agents.utils import test_utils
label_desc: Use image node_id 10 for calling the LinearBanditPolicyTest obj's underlying member method code with example usage: obj._time_step_batch_with_mask(batch_size) and returns: ts