input: Write a Python function to build and compile a sequential model with an embedding layer, two LSTM layers, and two dense layers.
  assert isinstance(build_model(), tf.keras.models.Sequential)
  assert len(build_model().layers) == 5
  assert build_model().layers[0].output_dim == 64
  assert build_model().layers[1].units == 32
  assert build_model().layers[2].units == 32
  assert build_model().layers[3].units == 64
  assert build_model().layers[4].activation == 'sigmoid'
output: def build_model():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Embedding, LSTM, Dense
      
      model = Sequential()
      model.add(Embedding(input_dim=10000, output_dim=64))
      model.add(LSTM(units=32, return_sequences=True))
      model.add(LSTM(units=32))
      model.add(Dense(units=64, activation='relu'))
      model.add(Dense(units=1, activation='sigmoid'))
      
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      return model