我按照tutorial here来尝试使用CIFAR-100训练我的模型。但是我得到了这个错误。我该怎么办？

ValueError: Data Params Error: The dataset label shape (100,) does not match the
                number of classes (10) in the dataset. Please ensure the dataset
                labels have 10 classes, or change the number of classes
                to match the dataset.

这是我的设置，改编自教程，但适用于CIFAR-100。

import tensorflow as tf
import tensorflow_datasets as tfds

import masterful

masterful = masterful.register()

TRAINING_PERCENTAGE = 5
(training_dataset,
 test_dataset) = tfds.load('cifar100',
                           as_supervised=True,
                           split=[f'train[:{TRAINING_PERCENTAGE}%]', 'test'],
                           with_info=False)

def sparse_to_dense(image, label):
  label = tf.cast(label, tf.int32)
  one_hot_label = tf.one_hot(label, depth=100)
  return image, one_hot_label

training_dataset = training_dataset.map(sparse_to_dense,
                                        num_parallel_calls=tf.data.AUTOTUNE)
test_dataset = test_dataset.map(sparse_to_dense,
                                num_parallel_calls=tf.data.AUTOTUNE)

def get_model():
  model = tf.keras.models.Sequential()
  model.add(
      tf.keras.layers.experimental.preprocessing.Rescaling(1. / 255,
                                                           input_shape=(32, 32,
                                                                        3)))
  model.add(tf.keras.layers.Conv2D(
      16,
      (3, 3),
      activation='relu',
  ))
  model.add(tf.keras.layers.GlobalAveragePooling2D())
  model.add(tf.keras.layers.Dense(10))
  return model

model = get_model()

model_params = masterful.architecture.learn_architecture_params(
    model=model,
    task=masterful.enums.Task.CLASSIFICATION,
    input_range=masterful.enums.ImageRange.CIFAR10_TORCH,
    num_classes=10,
    prediction_logits=True,
)
training_dataset_params = masterful.data.learn_data_params(
    dataset=training_dataset,
    task=masterful.enums.Task.CLASSIFICATION,
    image_range=masterful.enums.ImageRange.CIFAR10_TORCH,
    num_classes=10,
    sparse_labels=False,
)

optimization_params = masterful.optimization.learn_optimization_params(
    model,
    model_params,
    training_dataset,
    training_dataset_params,
)

# This is a set of parameters learned on CIFAR10 for
# small sized models.
regularization_params = masterful.regularization.parameters.CIFAR10_SMALL

training_report = masterful.training.train(
    model,
    model_params,
    optimization_params,
    regularization_params,
    None,
    training_dataset,
    training_dataset_params,
)