input: Write a python function to create and compile a neural network model using TensorFlow.
  assert isinstance(create_and_compile_model().layers[0], tf.keras.layers.Dense)
  assert create_and_compile_model().layers[0].input_shape == (None, 2)
  assert create_and_compile_model().loss == 'binary_crossentropy'
  assert create_and_compile_model().metrics_names == ['loss', 'accuracy']
output: def create_and_compile_model():
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(10, activation='relu', input_shape=(2,)))
      model.add(Dense(1, activation='sigmoid'))
      
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      
      return model