将client_dataset = emnist_train.create_tf_dataset_for_client( emnist_train.client_ids[i])改写为以fashion_mnist为数据集的代码

Sure, 下面是以fashion_mnist为数据集的代码：

import tensorflow as tf
from tensorflow import keras
from tensorflow_federated import datasets

fashion_mnist_train, fashion_mnist_test = datasets.tffashion_mnist.load_data()

def get_fashion_mnist_datasets():
  def preprocess(dataset):
    def batch_format_fn(element):
      return (tf.reshape(element['pixels'], [-1, 784]), element['label'])

    return dataset.repeat(NUM_EPOCHS)    .shuffle(SHUFFLE_BUFFER)    .batch(BATCH_SIZE)    .map(batch_format_fn)

  return  preprocess(fashion_mnist_train), preprocess(fashion_mnist_test)

client_dataset = get_fashion_mnist_datasets()[0].create_tf_dataset_for_client(fashion_mnist_train.client_ids[i])

以上代码假设已经有定义了NUM_EPOCHS、SHUFFLE_BUFFER和BATCH_SIZE的变量，若没有定义需要自定义变量。