input: Write a python function to create and train a Sequential model with LSTM, Dense, Dropout, and compile it.
  assert type(create_and_train_model(X_train, y_train, n_timesteps, n_features, n_outputs)) == Sequential
output: def create_and_train_model(X_train, y_train, n_timesteps, n_features, n_outputs):
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import LSTM, Dense, Dropout
      from tensorflow.keras.optimizers import Adam
      
      # Define the model
      model = Sequential()
      model.add(LSTM(50, activation='relu', input_shape=(n_timesteps, n_features)))
      model.add(Dropout(0.2))
      model.add(Dense(50, activation='relu'))
      model.add(Dense(n_outputs, activation='softmax'))
      
      # Compile the model
      model.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
      
      # Train the model
      model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
      
      return model