code: def test_is_pickleable(self, batch_size, hidden_sizes):
      env_spec = GymEnv(DummyBoxEnv())
      obs_dim = env_spec.observation_space.flat_dim
      obs = torch.ones([batch_size, obs_dim], dtype=torch.float32)
  
      policy = DeterministicMLPPolicy(
          env_spec=env_spec,
          hidden_nonlinearity=None,
          hidden_sizes=hidden_sizes,
          hidden_w_init=nn.init.ones_,
          output_w_init=nn.init.ones_,
      )
  
      output1 = policy.get_actions(obs)[0]
  
      p = pickle.dumps(policy)
      policy_pickled = pickle.loads(p)
      output2 = policy_pickled.get_actions(obs)[0]
      assert np.array_equal(output1, output2)
  
file_path: garage/tests/garage/torch/policies/test_deterministic_mlp_policy.py
imports: import pickle
  import numpy
  import pytest
  import torch
  from torch import nn
  from garage.envs import GymEnv
  from garage.torch.policies import DeterministicMLPPolicy
  from tests.fixtures.envs.dummy import DummyBoxEnv, DummyDictEnv
label_desc: Use image node_id 3 for calling the TestDeterministicMLPPolicies obj's underlying member method code with example usage: obj.test_is_pickleable(batch_size, hidden_sizes) without return types