input: Write a python function to create, compile, and fit a simple neural network model using Keras.
  assert isinstance(create_and_train_model(X_train, y_train), keras.engine.training.Model)
  assert create_and_train_model(X_train, y_train).count_params() > 0
output: def create_and_train_model(X_train, y_train):
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.optimizers import Adam
      
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))
  
      model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
      
      return model