code: def test_tabtransformer_combiner_number_or_binary_with_category(
      features_to_test: tuple,
      embed_input_feature_name: Optional[Union[int, str]],
      fc_layers: Optional[list],
      reduce_output: str,
      num_layers: int,
  ) -> None:
      # make repeatable
      set_random_seed(RANDOM_SEED)
  
      # retrieve simulated encoder outputs and input features for the test
      encoder_outputs, input_features = features_to_test
  
      # setup combiner to test
      combiner = TabTransformerCombiner(
          input_features=input_features,
          config=load_config(
              TabTransformerCombinerConfig,
              embed_input_feature_name=embed_input_feature_name,
              # emulates parameters passed from combiner def
              num_layers=num_layers,  # number of transformer layers
              fc_layers=fc_layers,  # fully_connected layer definition
              reduce_output=reduce_output,  # sequence reducer
          ),
      ).to(DEVICE)
  
      # concatenate encoder outputs
      combiner_output = combiner(encoder_outputs)
  
      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,),
          target,
      )
  
      # Adjustments to the trainable parameter count (tpc) in the following assertion checks is needed
      # to account for the different code paths taken in the TabTransformerCombiner forward() method due to the
      # combination of input feature types (NUMBER, BINARY, CATEGORY) in the dataset and parameters used to
      # instantiate the TabTransformerCombiner object.
  
      # This test does not explicity test for a single categorical input feature
      # in this situation of a one categorical input feature, the query and key parameters are not updated
  
      assert (
          upc == tpc
      ), 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 15 for calling a global function with example usage: test_tabtransformer_combiner_number_or_binary_with_category(features_to_test, embed_input_feature_name, fc_layers, reduce_output, num_layers) without return types