r"""Base class for all neural network modules. Your models should also subclass this class. Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:: import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self): super(Model, self).__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x)) Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:`to`, etc.

这是关于PyTorch中神经网络模块的基类`nn.Module`的说明文档。所有的神经网络模型都应该继承这个类。`nn.Module`还可以包含其他的`nn.Module`，从而允许将它们嵌套在树形结构中。可以将子模块分配为常规属性，这些子模块将被注册，并在调用`to`等方法时将它们的参数也进行转换。在示例中，`Model`类继承了`nn.Module`类，并包含了两个卷积层子模块`conv1`和`conv2`，在前向传递方法`forward`中使用`F.relu`进行激活函数的处理。