How to use the kaggler.preprocessing.EmbeddingEncoder function in Kaggler

X_emb = ee.fit_transform(X=df[feature_cols], y=df[TARGET_COL])
    assert X_emb.shape[1] == sum(ee.n_emb)

    print('Test with the binary classification target')
    df[TARGET_COL] = (df[TARGET_COL] > df[TARGET_COL].mean()).astype(int)

    ee = EmbeddingEncoder(cat_cols=cat_cols,
                          num_cols=num_cols,
                          random_state=RANDOM_SEED)

    X_emb = ee.fit_transform(X=df[feature_cols], y=df[TARGET_COL])
    assert X_emb.shape[1] == sum(ee.n_emb)

    print('Test with the binary classification target with cross validation')
    cv = KFold(n_splits=N_FOLD, shuffle=True, random_state=RANDOM_SEED)
    ee = EmbeddingEncoder(cat_cols=cat_cols,
                          num_cols=num_cols,
                          cv=cv,
                          random_state=RANDOM_SEED)

    X_emb = ee.fit_transform(X=df[feature_cols], y=df[TARGET_COL])
    assert X_emb.shape[1] == sum(ee.n_emb)

feature_cols = [x for x in df.columns if x != TARGET_COL]
    cat_cols = [x for x in feature_cols if df[x].nunique() < 100]
    num_cols = [x for x in feature_cols if x not in cat_cols]

    print('Test with the regression target')
    ee = EmbeddingEncoder(cat_cols=cat_cols,
                          num_cols=num_cols,
                          random_state=RANDOM_SEED)

    X_emb = ee.fit_transform(X=df[feature_cols], y=df[TARGET_COL])
    assert X_emb.shape[1] == sum(ee.n_emb)

    print('Test with the binary classification target')
    df[TARGET_COL] = (df[TARGET_COL] > df[TARGET_COL].mean()).astype(int)

    ee = EmbeddingEncoder(cat_cols=cat_cols,
                          num_cols=num_cols,
                          random_state=RANDOM_SEED)

    X_emb = ee.fit_transform(X=df[feature_cols], y=df[TARGET_COL])
    assert X_emb.shape[1] == sum(ee.n_emb)

    print('Test with the binary classification target with cross validation')
    cv = KFold(n_splits=N_FOLD, shuffle=True, random_state=RANDOM_SEED)
    ee = EmbeddingEncoder(cat_cols=cat_cols,
                          num_cols=num_cols,
                          cv=cv,
                          random_state=RANDOM_SEED)

    X_emb = ee.fit_transform(X=df[feature_cols], y=df[TARGET_COL])
    assert X_emb.shape[1] == sum(ee.n_emb)