input: Write a python function to create and compile a CNN model for binary classification.
  assert isinstance(create_and_compile_model(), Sequential)
  assert create_and_compile_model().count_params() > 0
output: def create_and_compile_model():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
      
      model = Sequential([
          Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
          MaxPooling2D(pool_size=(2, 2)),
          Conv2D(64, (3, 3), activation='relu'),
          MaxPooling2D(pool_size=(2, 2)),
          Flatten(),
          Dense(128, activation='relu'),
          Dense(1, activation='sigmoid')
      ])
      
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      
      return model