input: Write a python function to create and compile a Keras model for image classification using Conv2D, MaxPooling2D, Dropout, Flatten, and Dense layers. The input shape should be (64, 64, 3), and it should use 'binary_crossentropy' loss and 'adam' optimizer.
  assert model.input_shape == (None, 64, 64, 3)
  assert model.output_shape == (None, 1)
output: def create_compile_model():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
  
      model = Sequential([
          Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
          MaxPooling2D(pool_size=(2, 2)),
          Dropout(0.25),
          Conv2D(64, (3, 3), activation='relu'),
          MaxPooling2D(pool_size=(2, 2)),
          Dropout(0.25),
          Conv2D(128, (3, 3), activation='relu'),
          MaxPooling2D(pool_size=(2, 2)),
          Dropout(0.25),
          Flatten(),
          Dense(128, activation='relu'),
          Dropout(0.5),
          Dense(1, activation='sigmoid')
      ])
  
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      
      return model
  
  model = create_compile_model()