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. The model should use a convolutional neural network with layers described below. Return None once the model is compiled and fitted.
  assert collate_model() is None
output: def collate_model():
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
      from tensorflow.keras.preprocessing.image import ImageDataGenerator
  
      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'])
  
      train_datagen = ImageDataGenerator(rescale=1./255)
      train_generator = train_datagen.flow_from_directory(
          'dataset/train',
          target_size=(64, 64),
          batch_size=32,
          class_mode='binary'
      )
  
      model.fit(
          train_generator,
          steps_per_epoch=25,
          epochs=10
      )
  
      return None