code: def create_time_step(self, use_per_arm_features=False, num_arms=1):
      observation = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
      observation_spec = tensor_spec.TensorSpec(
          observation.shape.as_list(), tf.float32
      )
      if use_per_arm_features:
          # Create arm features with:
          # max_num_arms = 4, num_action = 2, per_arm_dim = 2.
          observation = {
              bandit_spec_utils.GLOBAL_FEATURE_KEY: observation,
              bandit_spec_utils.PER_ARM_FEATURE_KEY: tf.constant(
                  [[5, 6], [7, 8], [9, 10], [11, 12]], tf.float32
              ),
              bandit_spec_utils.NUM_ACTIONS_FEATURE_KEY: tf.constant(
                  num_arms, tf.int32
              ),
          }
          observation_spec = tf.nest.map_structure(
              lambda t: tensor_spec.TensorSpec(
                  t.shape.as_list(), t.dtype
              ),
              observation,
          )
  
      time_step = ts.restart(observation)
      time_step_spec = ts.time_step_spec(observation_spec)
  
      return time_step_spec, time_step
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.specs import utils
  from tf_agents.policies import random_tf_policy
  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 nest_utils
  from tf_agents.utils import test_utils
label_desc: Use image node_id 2 for calling the RandomTFPolicyTest obj's underlying member method code with example usage: obj.create_time_step(use_per_arm_features, num_arms) and returns: time_step_spec,  time_step