text: import tensorflow as tf
  from tensorflow.keras.utils import plot_model
  def tf_dense(input_shape):
      model = tf.keras.Sequential()
      model.add(tf.keras.layers.Dense(32, input_shape=input_shape))
      model.add(tf.keras.layers.Dense(64))
      model.add(tf.keras.layers.Flatten())
      model.add(tf.keras.layers.Dense(8, activation='softmax'))
  
      optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)
      model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
  
      return model
  
  def tf_lstm(input_shape):
      model = tf.keras.Sequential()
      model.add(tf.keras.layers.Permute((2, 1), input_shape=input_shape))
  
      model.add(tf.keras.layers.Conv1D(256,kernel_size=7,activation='relu'))
      model.add(tf.keras.layers.MaxPool1D(3))
      model.add(tf.keras.layers.Conv1D(256,kernel_size=7,activation='relu'))
      model.add(tf.keras.layers.MaxPool1D(3))
  
      model.add(tf.keras.layers.Dropout(0.5))
      model.add(tf.keras.layers.LSTM(256,activation='relu'))
      model.add(tf.keras.layers.Dense(64))
      model.add(tf.keras.layers.Dense(64))
      model.add(tf.keras.layers.Dropout(0.5))
      model.add(tf.keras.layers.Flatten())
      model.add(tf.keras.layers.Dense(8, activation='softmax'))
      model.summary()
      optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
      model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
      model.summary()
      return model