code: def test_forward_gives_correct_output(self):
      params = Params(
          {
              "input_dim": 2,
              "output_dims": 3,
              "pool_sizes": 4,
              "dropout": 0.0,
              "num_layers": 2,
          }
      )
      maxout = Maxout.from_params(params)
  
      constant_init = Initializer.from_params(
          Params({"type": "constant", "val": 1.0})
      )
      initializer = InitializerApplicator([(".*", constant_init)])
      initializer(maxout)
  
      input_tensor = torch.FloatTensor([[-3, 1]])
      output = maxout(input_tensor).data.numpy()
      assert output.shape == (1, 3)
      # This output was checked by hand
      # The output of the first maxout layer is [-1, -1, -1], since the
      # matrix multiply gives us [-2]*12. Reshaping and maxing
      # produces [-2, -2, -2] and the bias increments these values.
      # The second layer output is [-2, -2, -2], since the matrix
      # matrix multiply gives us [-3]*12. Reshaping and maxing
      # produces [-3, -3, -3] and the bias increments these values.
      assert_almost_equal(output, [[-2, -2, -2]])
  
file_path: allennlp/tests/modules/maxout_test.py
imports: 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 Maxout
  from allennlp.nn import InitializerApplicator, Initializer
  from allennlp.common.testing import AllenNlpTestCase
label_desc: Use image node_id 4 for calling the TestMaxout obj's underlying member method code with example usage: obj.test_forward_gives_correct_output() without return types