input: from typing import List
  
  def collate_ml_model(dataset_path: str, num_epochs: int, batch_size: int) -> Sequential:
      """ Given a path to a dataset of cat and dog images, collate a machine learning 
      model using Keras that distinguishes between cats and dogs. The model is based 
      on a convolutional neural network (CNN) with several layers. The function should 
      return the compiled model.
      >>> model = collate_ml_model('/path/to/dataset', 5, 32)
      >>> model.summary()  # This will print the structure of the model
      >>> model.fit_generator(...)  # This will return the result of fitting the model
      """
      import numpy as np
      import keras
      from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten
      from keras.models import Sequential
      from keras.preprocessing.image import ImageDataGenerator
  
      # Create the neural network model
      model = Sequential()
  
      # Input layer
      model.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
  
      # Hidden layers
      model.add(MaxPooling2D(pool_size = (2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation = 'relu'))
      model.add(MaxPooling2D(pool_size = (2, 2)))
      model.add(Dropout(0.3))
      model.add(Conv2D(64, (3, 3), activation = 'relu'))
      model.add(MaxPooling2D(pool_size = (2, 2)))
      model.add(Dropout(0.3))
  
      # Output layer
      model.add(Flatten())
      model.add(Dense(units = 128, activation = 'relu'))
      model.add(Dense(units = 1, activation = 'sigmoid'))
  
      # Compile the model
      model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
  
      # Create data generator
      datagen = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, zoom_range = 0.2, horizontal_flip = True)
  
      # Fit the model
      train_generator = datagen.flow_from_directory(directory = dataset_path, target_size = (64, 64), color_mode = 'rgb', class_mode = 'binary', batch_size = batch_size)
      model.fit_generator(generator = train_generator, steps_per_epoch = 25, epochs = num_epochs)
      
      return model
output:     
      return model
  