code: def test_function_transformer_works_with_categoricals():
      X = pd.DataFrame(
          {"col1": ["1", "2", "3"], "col2": ["a", "b", "c"]}
      )
  
      X_expected = pd.DataFrame(
          {"col1": [1.0, 2.0, 3.0], "col2": ["a", "b", "c"]}
      )
  
      transformer = SklearnTransformerWrapper(
          FunctionTransformer(lambda x: x.astype(np.float64)),
          variables=["col1"],
      )
  
      X_tf = transformer.fit_transform(X)
  
      pd.testing.assert_frame_equal(X_expected, X_tf)
  
imports: import numpy
  import pandas
  import pytest
  from sklearn.base import clone
  from sklearn.datasets import fetch_california_housing
  from sklearn.decomposition import PCA
  from sklearn.ensemble import RandomForestClassifier, VotingClassifier
  from sklearn.feature_selection import RFE, SelectFromModel, SelectKBest, VarianceThreshold, f_regression
  from sklearn.impute import MissingIndicator, SimpleImputer
  from sklearn.linear_model import Lasso
  from sklearn.model_selection import cross_val_score
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import Binarizer, FunctionTransformer, KBinsDiscretizer, MinMaxScaler, Normalizer, OneHotEncoder, OrdinalEncoder, PolynomialFeatures, PowerTransformer, StandardScaler
  from feature_engine.wrappers import SklearnTransformerWrapper
label_desc: Use image node_id 23 for calling a global function with example usage: test_function_transformer_works_with_categoricals() without return types