2、解释代码

请解释以下代码：import torch import torch.nn as nn # 定义输入层、隐藏层和输出层的神经元数量 input_layer_size = 4 hidden_layer_size = 5 output_layer_size = 3 # 定义前馈神经网络类 class FeedForwardNN(nn.Module): def init(self): super(FeedForwardNN, self).init() self.fc1 = nn.Linear(input_layer_size, hidden_layer_size) self.fc2 = nn.Linear(hidden_layer_size, output_layer_size) self.sigmoid = nn.Sigmoid() def forward(self, x): x = self.fc1(x) x = self.sigmoid(x) x = self.fc2(x) x = self.sigmoid(x) return x # 创建神经网络实例 model = FeedForwardNN() # 测试前馈函数 X = torch.randn(1, input_layer_size) y = model(X) print(y)