input: Write a python function to create and train a neural network model using TensorFlow.
  assert isinstance(create_and_train_model(x_train, y_train), tf.keras.Sequential)
  assert create_and_train_model(x_train, y_train).optimizer.get_config()['name'] == 'adam'
  assert create_and_train_model(x_train, y_train).loss == 'binary_crossentropy'
output: def create_and_train_model(x_train, y_train):
      import tensorflow as tf
  
      # Define the model
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(128, activation='relu', input_shape=(x_train.shape[1],)),
          tf.keras.layers.Dense(64, activation='relu'),
          tf.keras.layers.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, verbose=0)
  
      return model