Keras SKLearnClassifier wrapper can't fit MNIST data

I'm trying to use the SKLearnClassifier Keras wrapper to do some grid searching and cross validation using the sklearn library but I'm unable to get the model to work properly.

def build_model(X, y, n_neurons: List[str], learning_rate: float):
    model = keras.models.Sequential()
    model.add(keras.Input(shape=(28*28,)))
    model.add(keras.layers.Dense(n_neurons[0], activation="relu"))
    model.add(keras.layers.Dense(n_neurons[1], activation="relu"))
    model.add(keras.layers.Dense(10, activation="softmax"))

    optimizer = keras.optimizers.SGD(learning_rate=learning_rate)
    model.compile(loss="sparse_categorical_crossentropy",
                optimizer=optimizer,
                metrics=["accuracy"])
    return model

sk_train = X_train.reshape((X_train.shape[0],X_train.shape[1]*X_train.shape[2]))
sk_val = X_val.reshape((X_val.shape[0],X_val.shape[1]*X_val.shape[2]))

model = keras.wrappers.SKLearnClassifier(model=build_model, model_kwargs={
    "n_neurons": [300, 100],
    "learning_rate": 3e-4
})

model.fit(sk_train, y_train, epochs=30, validation_data=(sk_val, y_val))

The error I get is

ValueError: Argument `output` must have rank (ndim) `target.ndim - 1`. Received: target.shape=(None, 10), output.shape=(None, 10)

The error message seems to be saying that it expects an output of shape (None, 10) and that it received (None, 10), which doesn't make sense to me. The model works fine if I just call the function and fit the model directly, without the wrapper:

dummy_model = build_model(X_train, y_train, [300, 100], learning_rate=3e-4)
dummy_model.summary()
dummy_model.fit(sk_train, y_train, epochs=30, validation_data=(sk_val, y_val))

I've also tried not reshaping the data to keep the original 28*28 shape but it just makes it worse.