code: def test_transformer_combiner(
      encoder_outputs: tuple,
      transformer_output_size: int,
      output_size: int,
  ) -> None:
      # make repeatable
      set_random_seed(RANDOM_SEED)
  
      encoder_outputs_dict, input_feature_dict = encoder_outputs
  
      # setup combiner to test
      combiner = TransformerCombiner(
          input_features=input_feature_dict,
          config=load_config(TransformerCombinerConfig),
      ).to(DEVICE)
  
      # confirm correctness of input_shape property
      assert isinstance(combiner.input_shape, dict)
      for k in encoder_outputs_dict:
          assert k in combiner.input_shape
          assert (
              encoder_outputs_dict[k][ENCODER_OUTPUT].shape[1:]
              == combiner.input_shape[k]
          )
  
      # calculate expected hidden size for concatenated tensors
      hidden_size = 0
      for k in encoder_outputs_dict:
          hidden_size += np.prod(
              encoder_outputs_dict[k][ENCODER_OUTPUT].shape[1:]
          )
  
      # confirm correctness of effective_input_shape
      assert combiner.concatenated_shape[-1] == hidden_size
  
      # concatenate encoder outputs
      combiner_output = combiner(encoder_outputs_dict)
  
      # check for correctness of combiner output
      check_combiner_output(combiner, combiner_output, BATCH_SIZE)
  
      # check for parameter updating
      target = torch.randn(combiner_output["combiner_output"].shape)
      fpc, tpc, upc, not_updated = check_module_parameters_updated(
          combiner, (encoder_outputs_dict,), target
      )
      assert (
          tpc == upc
      ), f"Failed to update parameters. Parameters not updated: {not_updated}"
  
imports: import logging
  from collections import OrderedDict
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import pytest
  import torch
  from ludwig.combiners.combiners import ComparatorCombiner, ConcatCombiner, ProjectAggregateCombiner, SequenceCombiner, SequenceConcatCombiner, TabNetCombiner, TabTransformerCombiner, TransformerCombiner
  from ludwig.constants import CATEGORY, ENCODER_OUTPUT, ENCODER_OUTPUT_STATE, TYPE
  from ludwig.encoders.registry import get_sequence_encoder_registry
  from ludwig.schema.combiners.comparator import ComparatorCombinerConfig
  from ludwig.schema.combiners.concat import ConcatCombinerConfig
  from ludwig.schema.combiners.project_aggregate import ProjectAggregateCombinerConfig
  from ludwig.schema.combiners.sequence import SequenceCombinerConfig
  from ludwig.schema.combiners.sequence_concat import SequenceConcatCombinerConfig
  from ludwig.schema.combiners.tab_transformer import TabTransformerCombinerConfig
  from ludwig.schema.combiners.tabnet import TabNetCombinerConfig
  from ludwig.schema.combiners.transformer import TransformerCombinerConfig
  from ludwig.schema.utils import load_config
  from ludwig.utils.misc_utils import set_random_seed
  from ludwig.utils.torch_utils import get_torch_device
  from tests.integration_tests.parameter_update_utils import check_module_parameters_updated
label_desc: Use image node_id 10 for calling a global function with example usage: test_transformer_combiner(encoder_outputs, transformer_output_size, output_size) without return types