input: Write a function to collate a machine learning model in Python that distinguishes between cats and dogs using a dataset of 800 images of cats and dogs. Return the compiled model.
  assert type(collate_model())==keras.engine.sequential.Sequential
  assert collate_model().loss=='binary_crossentropy'
  assert collate_model().optimizer=='adam'
  assert collate_model().metrics==['accuracy']
output: def collate_model():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
      
      model = Sequential()
      model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Flatten())
      model.add(Dense(128, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))
      
      model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
      return model