import numpy as npimport torch# 导入 PyTorch 内置的 mnist 数据from torchvisiondatasets import mnist#导入预处理模块import torchvisiontransforms as transformsfrom torchutilsdata import DataLoader#导入nn及优化器import torch

这段代码有几处错误：

1. 在导入PyTorch库时，未导入torchvision库。
2. 在使用matplotlib绘图时，缺少对plt.subplot()函数的缩进。
3. plt.tight_layout()函数缩进不正确。
4. plt.imshow()函数中，应该将example_data[i][0]改为example_data[i][0].numpy()，因为imshow()函数需要接受numpy数组作为输入。

以下是修改后的代码：

import numpy as np
import torch
import torchvision
from torchvision.datasets import mnist
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim
from torch import nn

train_batch_size = 64
test_batch_size = 128
learning_rate = 0.01
num_epoches = 20
lr = 0.01
momentum = 0.5

transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize([0.5], [0.5])])

train_dataset = mnist.MNIST('./data', train=True, transform=transform, download=True)
test_dataset = mnist.MNIST('./data', train=False, transform=transform)

train_loader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=test_batch_size, shuffle=False)

import matplotlib.pyplot as plt
%matplotlib inline

examples = enumerate(test_loader)
batch_idx, (example_data, example_targets) = next(examples)
fig = plt.figure()
for i in range(6):
    plt.subplot(2,3,i+1)
    plt.tight_layout()
    plt.imshow(example_data[i][0].numpy(), cmap='gray', interpolation='none')
    plt.title("Ground Truth: {}".format(example_targets[i]))
    plt.xticks([])
    plt.yticks([])
``