code: def test_diff_shape_state(self):
      state = {}
      state["decoder_hidden"] = torch.tensor(
          [[1, 0, 1], [2, 0, 1], [0, 0, 1], [1, 1, 1], [0, 0, 0]]
      )
      state["decoder_hidden"] = (
          state["decoder_hidden"].unsqueeze(0).repeat(2, 1, 1)
      )
      # shape: (2, batch_size, 3)
  
      seq = [
          [1, 0, 1],
          [1, 0, 1],
          [1, 0, 1],
          [2, 0, 1],
          [2, 0, 1],
          [2, 0, 1],
          [0, 0, 1],
          [0, 0, 1],
          [0, 0, 1],
          [1, 1, 1],
          [1, 1, 1],
          [1, 1, 1],
          [0, 0, 0],
          [0, 0, 0],
          [0, 0, 0],
      ]
      seq = [seq] * 2
      expected_finished_state = {}
      expected_finished_state["decoder_hidden"] = np.array(seq)
      # shape: (2, batch_size x beam_size, 3)
  
      self._check_results(state=state)
  
      # check finished state.
      for key, array in expected_finished_state.items():
          np.testing.assert_allclose(state[key].numpy(), array)
  
imports: from typing import Dict, Tuple, Union
  import numpy
  import pytest
  import torch
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.nn.beam_search import MultinomialSampler, BeamSearch, TopKSampler, TopPSampler, GumbelSampler, LengthNormalizedSequenceLogProbabilityScorer, RepeatedNGramBlockingConstraint, StepFunctionTypeWithTimestep, StepFunctionTypeNoTimestep
  from allennlp.common.params import Params
  from allennlp.nn.util import min_value_of_dtype
label_desc: Use image node_id 5 for calling the BeamSearchTest obj's underlying member method code with example usage: obj.test_diff_shape_state() without return types