code: def testAutomaticReset(self):
      observation_spec = tensor_spec.TensorSpec([1], tf.float32)
      action_spec = tensor_spec.TensorSpec([1], tf.float32)
      policy_state_spec = tensor_spec.TensorSpec([1], tf.float32)
      time_step_spec = ts.time_step_spec(observation_spec)
  
      policy = TfPassThroughPolicy(
          time_step_spec,
          action_spec,
          policy_state_spec=policy_state_spec,
          automatic_state_reset=True,
      )
  
      observation = tf.constant(1, dtype=tf.float32, shape=(1, 1))
      reward = tf.constant(1, dtype=tf.float32, shape=(1,))
      time_step = tf.nest.map_structure(
          lambda *t: tf.concat(t, axis=0),
          ts.restart(observation, batch_size=1),
          ts.transition(observation, reward),
          ts.termination(observation, reward),
      )
  
      state = self.evaluate(
          policy.action(
              time_step, policy_state=policy.get_initial_state(3) + 1
          ).state
      )
  
      self.assertEqual(0, state[0])
      self.assertEqual(1, state[1])
      self.assertEqual(1, state[2])
  
      state = self.evaluate(
          policy.distribution(
              time_step, policy_state=policy.get_initial_state(3) + 1
          ).state
      )
  
      self.assertEqual(0, state[0])
      self.assertEqual(1, state[1])
      self.assertEqual(1, state[2])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import cast
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.policies import tf_policy
  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
  from tf_agents.utils import test_utils
label_desc: Use image node_id 11 for calling the TfPolicyTest obj's underlying member method code with example usage: obj.testAutomaticReset() without return types