code: def test_forward_pass(self):
      torch.manual_seed(123)
      state_dim = 1
      action_dim = 2
      state = rlt.FeatureData(torch.tensor([[2.0]]))
      bcq_drop_threshold = 0.20
  
      q_network = FullyConnectedDQN(
          state_dim, action_dim, sizes=[2], activations=["relu"]
      )
      init.constant_(q_network.fc.dnn[-1][-2].bias, 3.0)
      imitator_network = FullyConnectedNetwork(
          layers=[state_dim, 2, action_dim],
          activations=["relu", "linear"],
      )
  
      imitator_probs = torch.nn.functional.softmax(
          imitator_network(state.float_features), dim=1
      )
      bcq_mask = imitator_probs < bcq_drop_threshold
      npt.assert_array_equal(bcq_mask.detach(), [[True, False]])
  
      model = BatchConstrainedDQN(
          state_dim=state_dim,
          q_network=q_network,
          imitator_network=imitator_network,
          bcq_drop_threshold=bcq_drop_threshold,
      )
      final_q_values = model(state)
      npt.assert_array_equal(final_q_values.detach(), [[-1e10, 3.0]])
  
imports: import logging
  import unittest
  import numpy.testing
  import torch
  import torch.nn
  import torch.nn.init
  from reagent.core import types
  from reagent.models.bcq import BatchConstrainedDQN
  from reagent.models.dqn import FullyConnectedDQN
  from reagent.models.fully_connected_network import FullyConnectedNetwork
  from reagent.test.models.test_utils import run_model_jit_trace
label_desc: Use image node_id 4 for calling the TestBCQ obj's underlying member method code with example usage: obj.test_forward_pass() without return types