IndexError: Only support integers, slices(`:`), ellipsis(`...`), None, bool, tensor with int, list and tuple ,but got 0 with type <class 'numpy.int64'>

import os
import csv
import time
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
 
import mindspore as ms
import mindspore.dataset as ds
import mindspore.context as context
# import mindspore.dataset.transforms.c_transforms as C
# import mindspore.dataset.transforms.vision.c_transforms as CV
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV


from mindspore import nn, Tensor
from mindspore.train import Model
from mindspore.nn.metrics import Accuracy, MAE, MSE
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
 
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')

#加载并观察数据集，为数据处理做准备
with open('./auto-mpg.data') as csv_file:
    data = list(csv.reader(csv_file, delimiter=','))

#MPG:Miles Per Gallon,每加仑燃油可以跑多少英里
column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',
                'Acceleration', 'Model Year', 'Origin']

#利用pandas读取数据并对其进行初步处理，如：遇到？换成nan，忽略\t之后的内容等
raw_data = pd.read_csv("./auto-mpg.data", header=None, names=column_names, sep=r'\s+', na_values='?', usecols=['MPG','Cylinders','Displacement','Horsepower','Weight',
                'Acceleration', 'Model Year', 'Origin'])

data = raw_data.copy()

#对于数据集中的空值，我们要进行处理。
#代码需自己补充
data = data.dropna()

#查看训练数据集的结构，代码需自己补充
origin = data.pop("Origin")
labels = data.pop("MPG")
states = data.describe()
states = states.transpose()

#对特征进行归一化
def norm(x):
    return (x-states['mean'])/states['std']

normed_data = norm(data)
# normed_data["Origin"] = origin
normed_data["MPG"] = labels

#将数据集按照4：1划分成训练集和测试集
train_dataset = normed_data.sample(frac=0.8,random_state=0)
test_dataset = normed_data.drop(train_dataset.index)

#模型训练需要区分特征值与目标值，也就是我们常说的X值与Y值，此处MPG为Y值，其余的特征为X值。
train_labels = train_dataset.pop('MPG')
test_labels = test_dataset.pop('MPG')
X_train, Y_train = np.array(train_dataset), np.array(train_labels)
X_test, Y_test = np.array(test_dataset), np.array(test_labels)

#将数据集转换为Tensor格式
ds_xtrain = Tensor(X_train, ms.float32)
ds_ytrain = Tensor(Y_train, ms.int32)
print(ds_xtrain)
print(ds_ytrain)

ds_xtest = Tensor(X_test, ms.float32)
ds_ytest = Tensor(Y_test, ms.int32)

class Dataset(object):
    def __init__(self, xdata, ydata):
        self.xdata = xdata
        self.ydata = ydata

    def __getitem__(self, index):
        return [self.xdata[index], self.ydata[index]]

    def __len__(self):
        return len(self.xdata)


print(ds_xtrain[0])
gendataset = Dataset(ds_xtrain, ds_ytrain)
gendataset = ds.GeneratorDataset(gendataset, ["data", "label"], shuffle=False)
for data in gendataset.create_dict_iterator():
    print('{}'.format(data["data"]), '{}'.format(data["label"]))