the following code is from the bart-bikling example,I changed it to a classifier.but it reports the shape mismatch issue,seems that the set_data(X_test) not work.

```
from pathlib import Path
import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc as pm
import pymc_bart as pmb
from sklearn.model_selection import train_test_split
bikes = pd.read_csv(pm.get_data("bikes.csv"))
features = ["hour", "temperature", "humidity", "workingday"]
X = bikes[features]
Y = bikes["count"]
Y2 = Y.apply(lambda x:1 if x>180 else 0)
RANDOM_SEED=100
X_train, X_test, Y_train, Y_test = train_test_split(X, Y2, test_size=0.2, random_state=RANDOM_SEED)
with pm.Model() as model_oos_regression:
X1 = pm.MutableData("X", X_train.values)
Y1 = Y_train.values.flatten()
#α = pm.Exponential("α", 1)
μ = pmb.BART("μ", X1, Y1)
#y = pm.NegativeBinomial("y", mu=pm.math.exp(μ), alpha=α, observed=Y, shape=μ.shape)
#y = pm.Deterministic("y", pm.invlogit(μ))
pm.Bernoulli("y",observed=Y1,p=pm.Deterministic("p1", pm.invlogit(μ)))
idata = pm.sample(random_seed=RANDOM_SEED)
#idata_oos_regression = pm.fit(method=pm.ADVI()).sample()
#predict out sample
pm.set_data({"X":X_test.values})
# posterior_predictive_oos_regression_test = pm.sample_posterior_predictive(
# trace=idata_oos_regression, random_seed=RANDOM_SEED,
# var_names=['y'],
# return_inferencedata=True,
# predictions=True
# )
idata.extend(pm.sample_posterior_predictive(idata))
#pred = posterior_predictive_oos_regression_test.predictions
yHat = idata.posterior_predictive['y'].mean(("chain", "draw")).to_numpy()
print(f"yHat-len={len(yHat)},X_test-len={len(X_test)}")
assert len(yHat)==len(X_test)
```