input: Write a python function to create a Keras Sequential model with specific layers.
  assert create_model().layers[0].__class__.__name__ == 'Embedding'
  assert create_model().layers[1].__class__.__name__ == 'SpatialDropout1D'
  assert create_model().layers[2].__class__.__name__ == 'Conv1D'
  assert create_model().layers[3].__class__.__name__ == 'GlobalMaxPooling1D'
  assert create_model().layers[4].__class__.__name__ == 'Dense'
  assert create_model().layers[5].__class__.__name__ == 'Dropout'
  assert create_model().layers[6].__class__.__name__ == 'Dense'
output: def create_model():
      from keras.models import Sequential
      from keras.layers import Embedding, SpatialDropout1D, Conv1D, GlobalMaxPooling1D, Dense, Dropout
  
      model = Sequential()
      model.add(Embedding(input_dim=10000, output_dim=128, input_length=100))
      model.add(SpatialDropout1D(0.2))
      model.add(Conv1D(filters=64, kernel_size=3, padding='same', activation='relu'))
      model.add(GlobalMaxPooling1D())
      model.add(Dense(64, activation='relu'))
      model.add(Dropout(0.5))
      model.add(Dense(1, activation='sigmoid'))
      
      return model