@staticmethod
def build(rows, columns, units, classes):
    model = tf.keras.Sequential()
    input_shape = (rows, columns)
    model.add(tf.keras.layers.InputLayer(input_shape=input_shape))
    model.add(tf.keras.layers.LSTM(units=units, activation='tanh', return_sequences=True))
    model.add(tf.keras.layers.Dropout(rate=0.2))
    model.add(tf.keras.layers.LSTM(units=(units // 2), activation='tanh'))
    model.add(tf.keras.layers.Dropout(rate=0.2))
    model.add(tf.keras.layers.Dense(64, activation='relu'))
    model.add(tf.keras.layers.Dense(classes, activation='softmax'))
    return model
@staticmethod
def build(rows, columns, units, classes):
    model = tf.keras.Sequential()
    input_shape = (rows, columns)
    model.add(tf.keras.layers.InputLayer(input_shape=input_shape))
    model.add(tf.keras.layers.LSTM(units=units, activation='tanh', return_sequences=True))
    model.add(tf.keras.layers.Dropout(rate=0.2))
    model.add(tf.keras.layers.LSTM(units=(units // 2), activation='tanh'))
    model.add(tf.keras.layers.Dropout(rate=0.2))
    model.add(tf.keras.layers.Dense(64, activation='relu'))
    model.add(tf.keras.layers.Dense(classes, activation='softmax'))
    return model