minibatch_x = pm.Minibatch(X_train, batch_size=50)
minibatch_y = pm.Minibatch(Y_train, batch_size=50)

def construct_nn(ann_input, ann_output):
    n_hidden = 5

    # Initialize random weights between each layer
    init_1 = rng.standard_normal(size=(X_train.shape[1], n_hidden)).astype(floatX)
    init_2 = rng.standard_normal(size=(n_hidden, n_hidden)).astype(floatX)
    init_out = rng.standard_normal(size=n_hidden).astype(floatX)

    coords = {
        "hidden_layer_1": np.arange(n_hidden),
        "hidden_layer_2": np.arange(n_hidden),
        "train_cols": np.arange(X_train.shape[1]),
        # "obs_id": np.arange(X_train.shape[0]),
    }
    with pm.Model(coords=coords) as neural_network:
        ann_input = pm.Data("ann_input", X_train, mutable=True, dims=("obs_id", "train_cols"))
        ann_output = pm.Data("ann_output", Y_train, mutable=True, dims="obs_id")

minibatch_x, minibatch_y = pm.Minibatch(X_train, Y_train, batch_size=50)