code: def test_forward(self, hidden_sizes):
      env_spec = GymEnv(DummyBoxEnv()).spec
      obs_dim = env_spec.observation_space.flat_dim
      act_dim = env_spec.action_space.flat_dim
      obs = torch.ones(obs_dim, dtype=torch.float32).unsqueeze(0)
      act = torch.ones(act_dim, dtype=torch.float32).unsqueeze(0)
  
      qf = ContinuousMLPQFunction(
          env_spec=env_spec,
          hidden_nonlinearity=None,
          hidden_sizes=hidden_sizes,
          hidden_w_init=nn.init.ones_,
          output_w_init=nn.init.ones_,
      )
  
      output = qf(obs, act)
      expected_output = torch.full(
          [1, 1],
          fill_value=(obs_dim + act_dim) * np.prod(hidden_sizes),
          dtype=torch.float32,
      )
      assert torch.eq(output, expected_output)
  
file_path: garage/tests/garage/torch/q_functions/test_continuous_mlp_q_function.py
imports: import pickle
  import numpy
  import pytest
  import torch
  from torch import nn
  from garage.envs import GymEnv
  from garage.torch.q_functions import ContinuousMLPQFunction
  from tests.fixtures.envs.dummy import DummyBoxEnv
label_desc: Use image node_id 1 for calling the TestContinuousNNQFunction obj's underlying member method code with example usage: obj.test_forward(hidden_sizes) without return types