why is there no forward method in the code of training loop in the section 2?

[pippipincs]

torch.manual_seed(42)

Set the number of epochs

epochs = 1000

Put data on the available device

Without this, error will happen (not all model/data on device)

X_train = X_train.to(device)
X_test = X_test.to(device)
y_train = y_train.to(device)
y_test = y_test.to(device)

for epoch in range(epochs):
### Training
model_1.train() # train mode is on by default after construction

# 1. Forward pass
y_pred = model_1(X_train)

# 2. Calculate loss
loss = loss_fn(y_pred, y_train)

# 3. Zero grad optimizer
optimizer.zero_grad()

# 4. Loss backward
loss.backward()

# 5. Step the optimizer
optimizer.step()

### Testing
model_1.eval() # put the model in evaluation mode for testing (inference)
# 1. Forward pass
with torch.inference_mode():
    test_pred = model_1(X_test)

    # 2. Calculate the loss
    test_loss = loss_fn(test_pred, y_test)

if epoch % 100 == 0:
    print(f"Epoch: {epoch} | Train loss: {loss} | Test loss: {test_loss}")

I am wodering why forward method is not used instead they just simply put data into the object. Shouldn't we call the forward method of the object and then put data into it?