human
Solve the following python programming problem given the following code:
# Build a keras DNN model using Sequential API
def build_model(dnn_hidden_units):
    model = Sequential(DenseFeatures(feature_columns=feature_columns.values()))
    
    for num_nodes in dnn_hidden_units:
        model.add(Dense(units=num_nodes, activation="relu"))
    
    model.add(Dense(units=1, activation="linear"))    
    
    # Create a custom evaluation metric
    def rmse(y_true, y_pred):
        return tf.sqrt(tf.reduce_mean(tf.square(y_pred - y_true)))

    # Compile the keras model
    model.compile(optimizer="adam", loss="mse", metrics=[rmse, "mse"])
    
    return model
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Build a simple keras DNN model