9601.py

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer

def reduce_memory(X: pd.DataFrame, y=None):
    """Simple function to reduce memory usage by casting numeric columns to float32."""

    num_cols = X.select_dtypes(incluce=np.number).columns
    for col in num_cols:
        X[col] = X.astype("float32")

    return X, y

ReduceMemoryTransformer = FunctionTransformer(reduce_memory)

# Plug into a pipeline
>>> make_pipeline(SimpleImputer(), ReduceMemoryTransformer)

Pipeline(steps=[('simpleimputer', SimpleImputer()),
                ('functiontransformer', ReduceMemoryTransformer()])