code: def test_category_feature_vocab_size_1(feature_type, tmpdir) -> None:
      data_csv_path = os.path.join(tmpdir, "data.csv")
  
      input_feature = [category_feature(encoder={"vocab_size": 1})]
      output_feature = [binary_feature()]
  
      if feature_type == "output_feature":
          input_feature = output_feature
          output_feature = [category_feature(decoder={"vocab_size": 1})]
  
      config = {
          INPUT_FEATURES: input_feature,
          OUTPUT_FEATURES: output_feature,
          "training": {EPOCHS: 1},
      }
  
      training_data_csv_path = generate_data(
          config[INPUT_FEATURES], config[OUTPUT_FEATURES], data_csv_path
      )
  
      ludwig_model = LudwigModel(config)
      with pytest.raises(
          RuntimeError
      ) if feature_type == "output_feature" else contextlib.nullcontext():
          ludwig_model.train(dataset=training_data_csv_path)
  
imports: import contextlib
  import copy
  import logging
  import os
  import random
  import string
  from unittest import mock
  import numpy
  import pandas
  import pytest
  from PIL import Image
  from transformers import AutoTokenizer
  import ludwig
  from ludwig.api import LudwigModel
  from ludwig.backend import initialize_backend
  from ludwig.callbacks import Callback
  from ludwig.constants import BASE_MODEL, BATCH_SIZE, COLUMN, DECODER, EPOCHS, FULL, INPUT_FEATURES, MODEL_ECD, MODEL_LLM, MODEL_TYPE, NAME, OUTPUT_FEATURES, PREPROCESSING, PROC_COLUMN, PROMPT, SPLIT, TRAINER, TYPE
  from ludwig.data.concatenate_datasets import concatenate_df
  from ludwig.data.preprocessing import handle_features_with_prompt_config, preprocess_for_prediction
  from ludwig.schema.llms.prompt import PromptConfig
  from ludwig.schema.model_types.base import ModelConfig
  from tests.integration_tests.utils import assert_preprocessed_dataset_shape_and_dtype_for_feature, audio_feature, binary_feature, category_feature, generate_data, generate_data_as_dataframe, image_feature, LocalTestBackend, number_feature, sequence_feature, text_feature
label_desc: Use image node_id 20 for calling a global function with example usage: test_category_feature_vocab_size_1(feature_type, tmpdir) without return types