input: Write a python function to create a Sequential model with an Embedding layer, two LSTM layers, and a Dense layer for binary classification using TensorFlow's Keras API.
  assert isinstance(create_model(), tf.keras.Model) is True
  assert create_model().layers[0].output_dim == 50
  assert create_model().layers[1].units == 50
  assert create_model().layers[-1].activation.__name__ == 'sigmoid'
output: def create_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=50))
      model.add(LSTM(50, return_sequences=True))
      model.add(LSTM(50))
      model.add(Dense(1, activation='sigmoid'))
  
      return model