human
Solve the following python programming problem given the following code:
# Linear Regression Model
class LinearRegression(nn.Module):
    def __init__(self):
        super(LinearRegression, self).__init__()
        self.linear = nn.Linear(1, 1)  # input and output is 1 dimension

    def forward(self, x):
        out = self.linear(x)
        return out

model = LinearRegression()

# Define Loss and Optimizatioin function
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=1e-9)#1e-4)

num_epochs = 1000
for epoch in range(num_epochs):
    inputs = Variable(x_train)
    target = Variable(y_train)

    # forward
    out = model(inputs)
    loss = criterion(out, target)
    # backward
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (epoch+1) % 50 == 0:
        print('Epoch[{}/{}], loss: {:.6f}'
              .format(epoch+1, num_epochs, loss.data.item()))

Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

nn.Linear
Applies a linear transformation to the incoming data: $y = xA^T + b$