input: 
  def train_cat_dog_classifier(dataset_path: str, epochs: int, batch_size: int) -> keras.models.Sequential:
      """Train a machine learning model to distinguish between cats and dogs using the provided dataset path.
  
      >>> model = train_cat_dog_classifier('/path/to/dataset', 5, 32)
      >>> model.summary()
      """
  
output:     
      import tensorflow as tf
      from tensorflow.keras.preprocessing.image import ImageDataGenerator
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
  
      # Data preprocessing
      datagen = ImageDataGenerator(rescale=1./255)
      train_generator = datagen.flow_from_directory(
          dataset_path,
          target_size=(150, 150),
          batch_size=batch_size,
          class_mode='binary'
      )
  
      # Model architecture
      model = Sequential([
          Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
          MaxPooling2D(pool_size=(2, 2)),
          Conv2D(64, (3, 3), activation='relu'),
          MaxPooling2D(pool_size=(2, 2)),
          Conv2D(128, (3, 3), activation='relu'),
          MaxPooling2D(pool_size=(2, 2)),
          Flatten(),
          Dense(512, activation='relu'),
          Dense(1, activation='sigmoid')
      ])
  
      # Compile the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      # Train the model
      model.fit(
          train_generator,
          steps_per_epoch=train_generator.samples // batch_size,
          epochs=epochs
      )
  
      return model
  