how loss.backward connect to model

In PyTorch, the `loss.backward()` function connects the loss function to the model by calculating the gradients of the loss with respect to the model's parameters. This means that when you call `loss.backward()`, PyTorch computes the gradients of the loss with respect to every parameter in the model.

After calling `loss.backward()`, you can then use an optimizer (such as `torch.optim.SGD`) to update the model's parameters based on these gradients. The optimizer takes care of adjusting the values of the parameters in the direction of the negative gradient, which corresponds to minimizing the loss.

Here's an example code snippet that demonstrates how `loss.backward()` is used to update the model's parameters:

import torch
import torch.nn as nn
import torch.optim as optim

# Define a simple model
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc = nn.Linear(10, 1)

    def forward(self, x):
        return self.fc(x)

# Create a toy input and target
x = torch.randn(1, 10)
y = torch.randn(1, 1)

# Create an instance of the model and an optimizer
model = MyModel()
optimizer = optim.SGD(model.parameters(), lr=0.1)

# Compute the loss and gradients
y_hat = model(x)
loss = nn.MSELoss()(y_hat, y)
loss.backward()

# Update the model's parameters
optimizer.step()