input: def distinguish_cats_and_dogs_model(dataset_path: str) -> Sequential:
      """ Constructs and compiles a neural network model using Keras to distinguish 
      between images of cats and dogs. The dataset is loaded using ImageDataGenerator 
      and should be stored in a directory structure where each subdirectory is named 
      after a class (cats or dogs). The model is fit to the dataset and returned.
      
      Example:
      >>> model = distinguish_cats_and_dogs_model('/path/to/dataset')
      >>> model.summary()
      _________________________________________________________________
      Layer (type)                 Output Shape              Param #   
      =================================================================
      conv2d (Conv2D)              (None, 62, 62, 32)        896       
      _________________________________________________________________
      max_pooling2d (MaxPooling2D) (None, 31, 31, 32)        0         
      _________________________________________________________________
      dropout (Dropout)            (None, 31, 31, 32)        0         
      _________________________________________________________________
      conv2d_1 (Conv2D)            (None, 29, 29, 64)        18496     
      _________________________________________________________________
      max_pooling2d_1 (MaxPooling2 (None, 14, 14, 64)        0         
      _________________________________________________________________
      dropout_1 (Dropout)          (None, 14, 14, 64)        0         
      _________________________________________________________________
      conv2d_2 (Conv2D)            (None, 12, 12, 64)        36928     
      _________________________________________________________________
      max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         
      _________________________________________________________________
      dropout_2 (Dropout)          (None, 6, 6, 64)          0         
      _________________________________________________________________
      flatten (Flatten)            (None, 2304)              0         
      _________________________________________________________________
      dense (Dense)                (None, 128)               295040    
      _________________________________________________________________
      dense_1 (Dense)              (None, 1)                 129       
      =================================================================
      Total params: 351,489
      Trainable params: 351,489
      Non-trainable params: 0
      _________________________________________________________________
      """
output:     from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
      from tensorflow.keras.preprocessing.image import ImageDataGenerator
  
      # Define the model
      model = Sequential()
      model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.25))
  
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.25))
  
      model.add(Conv2D(64, (3, 3), activation='relu'))
      model.add(MaxPooling2D(pool_size=(2, 2)))
      model.add(Dropout(0.25))
  
      model.add(Flatten())
      model.add(Dense(128, activation='relu'))
      model.add(Dense(1, activation='sigmoid'))
  
      # Compile the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      # Data preprocessing
      datagen = ImageDataGenerator(rescale=1./255)
      train_generator = datagen.flow_from_directory(
          dataset_path,
          target_size=(64, 64),
          batch_size=32,
          class_mode='binary'
      )
  
      # Fit the model
      model.fit(
          train_generator,
          steps_per_epoch=train_generator.samples // train_generator.batch_size,
          epochs=10
      )
  
      return model