input: Write a python function to build and train a simple neural network using Keras.
  assert isinstance(build_and_train_model(np.array([[0, 0, 5], [1, 0, 5], [0, 1, 5], [0, 0, 6]]), np.array([[1], [0], [0], [0]])), Sequential)
  assert build_and_train_model(np.array([[0, 0, 5], [1, 0, 5], [0, 1, 5], [0, 0, 6]]), np.array([[1], [0], [0], [0]])).predict(np.array([[0, 0, 5]])) is not None
output: def build_and_train_model(X, y):
      from keras.models import Sequential
      from keras.layers import Dense
      from keras.optimizers import Adam
  
      # Initialize the model
      model = Sequential()
      
      # Add input layer and one hidden layer
      model.add(Dense(10, input_dim=X.shape[1], activation='relu'))
      
      # Add output layer
      model.add(Dense(1, activation='sigmoid'))
      
      # Compile the model
      model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])
      
      # Train the model
      model.fit(X, y, epochs=10, batch_size=1, verbose=0)
      
      return model