input: 
  from typing import Optional
  
  def collate_cat_dog_classifier() -> Optional[str]:
      """ Collate a machine learning model in Python that distinguishes between cats and dogs using Keras.
      Returns a string representation of the compiled model if successful, otherwise returns None.
      >>> collate_cat_dog_classifier()
      'Model: "sequential_1"\\n_________________________________________________________________\\nLayer (type)                 Output Shape              Param #   \\n=================================================================\\nconv2d_1 (Conv2D)            (None, 62, 62, 32)        896       \\n_________________________________________________________________\\n...\\n_________________________________________________________________\\nbinary_crossentropy, accuracy\'
      """
  
output:     from keras.models import Sequential
      from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
  
      try:
          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(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
          return str(model.to_json())
      except Exception as e:
          return None