如何创建自定义keras生成器以适应多个输出并使用工作进程

from keras.utils import Sequence
from keras.preprocessing.image import ImageDataGenerator
import numpy as np

class CustomDataGenerator(Sequence):
    def __init__(self, batch_size, img_paths, labels, num_outputs, num_classes):
        self.batch_size = batch_size
        self.img_paths = img_paths
        self.labels = labels
        self.num_outputs = num_outputs
        self.num_classes = num_classes
        self.image_data_generator = ImageDataGenerator()

    def __len__(self):
        return int(np.ceil(len(self.img_paths) / float(self.batch_size)))

    def __getitem__(self, idx):
        batch_img_paths = self.img_paths[idx * self.batch_size : (idx + 1) * self.batch_size]
        batch_labels = self.labels[idx * self.batch_size : (idx + 1) * self.batch_size]
        
        batch_images = []
        for img_path in batch_img_paths:
            img = self.image_data_generator.load_img(img_path, target_size=(224, 224))
            img = self.image_data_generator.img_to_array(img)
            batch_images.append(img)
            
        batch_images = np.array(batch_images)
        batch_labels = np.array(batch_labels)
        
        # 根据需要的输出数量进行修改
        batch_outputs = [batch_labels] * self.num_outputs
        
        return batch_images, batch_outputs

# 示例用法
img_paths = ["path/to/image1.jpg", "path/to/image2.jpg", ...]
labels = [[1, 0, 0], [0, 1, 0], ...] # 示例标签，可以根据实际任务进行修改
num_outputs = 2 # 需要的输出数量
num_classes = 3 # 分类任务的类别数量
batch_size = 32

data_generator = CustomDataGenerator(batch_size, img_paths, labels, num_outputs, num_classes)
images, outputs = data_generator[0]