code: def test_get_action(self, hidden_sizes):
      unflat_dim = (2, 2)
      env_spec = GymEnv(DummyBoxEnv(obs_dim=unflat_dim))
      obs_dim = env_spec.observation_space.flat_dim
      act_dim = env_spec.action_space.flat_dim
      obs = torch.ones(obs_dim, dtype=torch.float32)
      obs_unflat = torch.ones(unflat_dim, dtype=torch.float32)
      obs_np = np.ones(obs_dim, dtype=np.float32)
      obs_np_unflat = np.ones(unflat_dim, dtype=np.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_,
      )
  
      expected_output = np.full(
          act_dim,
          fill_value=obs_dim * np.prod(hidden_sizes),
          dtype=np.float32,
      )
      assert np.array_equal(policy.get_action(obs)[0], expected_output)
      assert np.array_equal(
          policy.get_action(obs_unflat)[0], expected_output
      )
      assert np.array_equal(
          policy.get_action(obs_np)[0], expected_output
      )
      assert np.array_equal(
          policy.get_action(obs_np_unflat)[0], expected_output
      )
  
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 1 for calling the TestDeterministicMLPPolicies obj's underlying member method code with example usage: obj.test_get_action(hidden_sizes) without return types