请给出一段代码表示TFF联邦学习中读取本地的fashionmnist数据集后如何分配到各个节点

好的，以下是一段示例代码，用于在TFF联邦学习中将FashionMNIST数据集分配到不同节点：

import tensorflow as tf
import tensorflow_federated as tff

# 定义一个获取FashionMNIST数据集的函数
def get_fashion_mnist():
    (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.fashion_mnist.load_data()
    # 将数据转换为浮点数，并将值缩放到0到1之间
    train_images = train_images / 255.0
    test_images = test_images / 255.0
    # 将标签转换为整数
    train_labels = train_labels.astype('int32')
    test_labels = test_labels.astype('int32')
    # 返回数据集
    return (train_images, train_labels), (test_images, test_labels)

# 获取FashionMNIST数据集
(train_images, train_labels), (test_images, test_labels) = get_fashion_mnist()

# 定义用于创建联邦数据集的函数
def create_federated_data():
  # 将数据转换为联邦学习格式
  train_data = [train_images[i:i+100], train_labels[i:i+100] for i in range(0, len(train_images), 100)]
  test_data = [test_images[i:i+100], test_labels[i:i+100] for i in range(0, len(test_images), 100)]
  # 创建联邦数据集
  train_federated_data = tff.simulation.client_data.ConcreteClientData(train_data)
  test_federated_data = tff.simulation.client_data.ConcreteClientData(test_data)
  # 返回联邦数据集
  return train_federated_data, test_federated_data

# 创建联邦数据集
train_federated_data, test_federated_data = create_federated_data()

# 定义一个获取联邦数据集的函数
def get_federated_data():
  return train_federated_data, test_federated_data

# 使用TensorFlow Federated库创建联邦学习模型并训练
federated_train_data, federated_test_data = get_federated_data()