input: Write a python function to build and train a neural network using Keras.
  assert build_and_train_model(np.array([[0.1, 0.2], [0.3, 0.4]]), np.array([[1, 0], [0, 1]]), 2) != None
  assert build_and_train_model(np.array([[0.5, 0.6], [0.7, 0.8]]), np.array([[0, 1], [1, 0]]), 2) != None
output: def build_and_train_model(X, y, epochs):
      from keras.models import Sequential
      from keras.layers import Dense
      
      # Define the model
      model = Sequential()
      model.add(Dense(10, input_dim=X.shape[1], activation='relu'))
      model.add(Dense(y.shape[1], activation='softmax'))
      
      # Compile the model
      model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
      
      # Train the model
      model.fit(X, y, epochs=epochs, verbose=0)
      
      return model