code: def test_digits_type_hints():
      data_bunch = load_digits()
  
      try:
          feature_names = data_bunch.feature_names
      except AttributeError:
          feature_names = [
              "x%d" % i for i in range(data_bunch.data.shape[1])
          ]
  
      data = data_df_from_bunch(data_bunch)
      data_clean = clean(
          data,
          type_hints={
              feature: "continuous" for feature in feature_names
          },
      )
      assert data_clean.shape[1] == 65
  
imports: import os
  import pytest
  import random
  import string
  import warnings
  import numpy
  import pandas
  from sklearn.datasets import load_iris, load_digits
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline
  from sklearn.linear_model import LogisticRegression
  from dabl.preprocessing import USELESS_TYPES, detect_types, EasyPreprocessor, DirtyFloatCleaner, clean, _FLOAT_REGEX, _float_matching, detect_type_series
  from dabl.utils import data_df_from_bunch
  from dabl.datasets import load_titanic
  from dabl import plot
label_desc: Use image node_id 22 for calling a global function with example usage: test_digits_type_hints() without return types