code: def test_decode(self):
      decoded_info = self.trainer.decode(
          self.initial_state, self.decoder_step, self.supervision
      )
      # The best state corresponds to the shortest path.
      assert decoded_info["best_action_sequences"][0] == [[0, 2, 4]]
      # The scores and costs corresponding to the finished states will be
      # [0, 2, 4] : -2, 0
      # [0, 1, 2, 4] : -3, 1
      # [0, 1, 3, 4] : -3, 2
      # [0, 2, 3, 4] : -3, 1
      # [0, 1, 2, 3, 4] : -4, 2
  
      # This is the normalization factor while re-normalizing probabilities on the beam
      partition = (
          np.exp(-2) + np.exp(-3) + np.exp(-3) + np.exp(-3) + np.exp(-4)
      )
      expected_loss = (
          (np.exp(-2) * 0)
          + (np.exp(-3) * 1)
          + (np.exp(-3) * 2)
          + (np.exp(-3) * 1)
          + (np.exp(-4) * 2)
      ) / partition
      assert_almost_equal(
          decoded_info["loss"].data.numpy(), expected_loss
      )
  
file_path: magnitude/pymagnitude/third_party/allennlp/tests/nn/decoding/decoder_trainers/expected_risk_minimization_test.py
imports: from __future__ import division
  from __future__ import absolute_import
  import torch
  import numpy
  from numpy.testing import assert_almost_equal
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.nn.decoding.decoder_trainers import ExpectedRiskMinimization
  from ..simple_transition_system import SimpleDecoderState, SimpleDecoderStep
label_desc: Use image node_id 3 for calling the TestExpectedRiskMinimization obj's underlying member method code with example usage: obj.test_decode() without return types