我定义了定制类，如下所示：

class MLPClassifier(nn.Module):
    """
    A basic multi-layer perceptron classifier with 3 layers.
    """
  
    def __init__(self, input_size, hidden_size, num_classes):
        """
        The constructor for the MLPClassifier class.
        """
        super(MLPClassifier, self).__init__()

        self.fc1 = nn.Linear(input_size, hidden_size)  # weights & biases for the input-to-hidden layer
        self.ac1 = nn.ReLU()                           # non-linear activation for the input-to-hidden layer
        self.fc2 = nn.Linear(hidden_size, num_classes) # weights & biases for the hidden-to-output layer
        self.ac2 = nn.Softmax(dim=1)                   # non-linear activation for the hidden-to-output layer

当我运行下面的脚本时，我得到了这个：

hyper_param_input_size  = 4
hyper_param_hidden_size = 64
hyper_param_num_classes = 3

model = MLPClassifier(hyper_param_input_size, hyper_param_hidden_size, hyper_param_num_classes)

for p in model.parameters():
    print(p.shape)

>>> torch.Size([64, 4])
>>> torch.Size([64])
>>> torch.Size([3, 64])
>>> torch.Size([3])

PyTorch究竟是如何自动知道我内部定义的属性的呢？我从来没有明确地告诉过它，它是否循环遍历类中的所有内容，并检查是否为isinstance(self, nn.Layer)或其他内容？