input: Write a python function to create, compile, and train a neural network using TensorFlow and Keras.
  assert type(create_and_train_model(X_train, y_train, X_test, y_test)) == float
output: def create_and_train_model(X_train, y_train, X_test, y_test):
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.optimizers import Adam
  
      # Create the model
      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'))
  
      # Compile the model
      model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])
  
      # Train the model
      model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))
  
      # Evaluate the model
      loss, accuracy = model.evaluate(X_test, y_test)
      
      return accuracy