code: def test_forward_gives_correct_output(self):
      params = Params(
          {
              "input_dim": 2,
              "hidden_dims": 3,
              "activations": "relu",
              "num_layers": 2,
          }
      )
      feedforward = FeedForward.from_params(params)
  
      constant_init = lambda tensor: torch.nn.init.constant_(
          tensor, 1.0
      )
      initializer = InitializerApplicator([(".*", constant_init)])
      initializer(feedforward)
  
      input_tensor = torch.FloatTensor([[-3, 1]])
      output = feedforward(input_tensor).data.numpy()
      assert output.shape == (1, 3)
      # This output was checked by hand - ReLU makes output after first hidden layer [0, 0, 0],
      # which then gets a bias added in the second layer to be [1, 1, 1].
      assert_almost_equal(output, [[1, 1, 1]])
  
file_path: magnitude/pymagnitude/third_party/allennlp/tests/modules/feedforward_test.py
imports: from __future__ import with_statement
  from __future__ import absolute_import
  from numpy.testing import assert_almost_equal
  import pytest
  import torch
  from allennlp.common import Params
  from allennlp.common.checks import ConfigurationError
  from allennlp.modules import FeedForward
  from allennlp.nn import InitializerApplicator
  from allennlp.common.testing import AllenNlpTestCase
label_desc: Use image node_id 3 for calling the TestFeedForward obj's underlying member method code with example usage: obj.test_forward_gives_correct_output() without return types