torch.normal函数用法
官方文档给的用法：
torch.normal(means, std, out=None)
意思为means给定tensor的均值范围和形状，std给出每个均值服从的标准差
官方示例：
torch.normal(means=torch.arange(1, 11), std=torch.arange(1, 0, -0.1))
1.5104
1.6955
2.4895
4.9185
4.9895
6.9155
7.3683
8.1836
8.7164
9.8916
[torch.FloatTensor of size 10]实际情况：
>>> torch.normal(means=torch.arange(1, 11), std=torch.arange(1, 0, -0.1))
Traceback (most recent call last):
File "", line 1, in
TypeError: normal() received an invalid combination of arguments - got (means=Tensor, std=Tensor, ), but expected one of:
* (Tensor mean, Tensor std, *, torch.Generator generator, Tensor out)
* (Tensor mean, float std, *, torch.Generator generator, Tensor out)
* (float mean, Tensor std, *, torch.Generator generator, Tensor out)
* (float mean, float std, tuple of ints size, *, torch.Generator generator, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad) 从错误类型中看出现在torch中已经不再使用means而是使用mean，即只能生成固定均值的正态分布。
正确的打开方式：
采用第四种：
torch.normal(mean, std, size)
三个参数分别为均值，标准差和size
>>> torch.normal(3, 0.1, (3, 4))
tensor([[2.9425, 3.1877, 2.9735, 3.0982],
[3.0061, 2.9918, 2.7953, 3.0066],
[2.8219, 2.9578, 2.8813, 2.9014]])采用第三种：
torch.normal(mean, stds)
两个参数分别为:均值和标准差，使用标准差来确定范围size
>>> torch.normal(3, torch.ones(3, 4)/10)
tensor([[2.8491, 3.0263, 3.0888, 3.0818],
[3.1101, 2.7490, 3.1847, 3.0861],
[2.8530, 2.8666, 2.9634, 3.1875]])