问使用PyTorch初始化权重和偏差-如何校正尺寸？

EN

dimensions_input = 10
hidden_layer_nodes = 5
output_dimension = 10

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear = torch.nn.Linear(dimensions_input,hidden_layer_nodes)
        self.linear2 = torch.nn.Linear(hidden_layer_nodes,output_dimension)

        self.linear.weight = torch.nn.Parameter(torch.zeros(dimensions_input,hidden_layer_nodes))
        self.linear.bias = torch.nn.Parameter(torch.ones(hidden_layer_nodes))

        self.linear2.weight = torch.nn.Parameter(torch.zeros(dimensions_input,hidden_layer_nodes))
        self.linear2.bias = torch.nn.Parameter(torch.ones(hidden_layer_nodes))

    def forward(self, x):
        l_out1 = self.linear(x)
        y_pred = self.linear2(l_out1)
        return y_pred

model = Model()

criterion = torch.nn.MSELoss(size_average = False)
optim = torch.optim.SGD(model.parameters(), lr = 0.00001)

def train_model():
    y_data = x_data.clone()
    for i in range(10000):
        y_pred = model(x_data)
        loss = criterion(y_pred, y_data)

        if i % 5000 == 0:
            print(loss)
        optim.zero_grad()

        loss.backward()
        optim.step()