Function train transfer learning

hello everyone, i’m in a big troble. does someone can explain to me this function line by line pleaaaase, it really blocks me i still a beginner

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()

best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0

for epoch in range(num_epochs):
    print('Epoch {}/{}'.format(epoch, num_epochs - 1))
    print('-' * 10)

    # Each epoch has a training and validation phase
    for phase in ['train', 'val']:
        if phase == 'train':
            model.train()  # Set model to training mode
            model.eval()   # Set model to evaluate mode

        running_loss = 0.0
        running_corrects = 0

        # Iterate over data.
        for inputs, labels in dataloaders[phase]:
            inputs =
            labels =

            # zero the parameter gradients

            # forward
            # track history if only in train
            with torch.set_grad_enabled(phase == 'train'):
                outputs = model(inputs)
                _, preds = torch.max(outputs, 1)
                loss = criterion(outputs, labels)

                # backward + optimize only if in training phase
                if phase == 'train':

            # statistics
            running_loss += loss.item() * inputs.size(0)
            running_corrects += torch.sum(preds ==
        if phase == 'train':

        epoch_loss = running_loss / dataset_sizes[phase]
        epoch_acc = running_corrects.double() / dataset_sizes[phase]

        print('{} Loss: {:.4f} Acc: {:.4f}'.format(
            phase, epoch_loss, epoch_acc))

        # deep copy the model
        if phase == 'val' and epoch_acc > best_acc:
            best_acc = epoch_acc
            best_model_wts = copy.deepcopy(model.state_dict())


time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
    time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))

# load best model weights
return model

It’s unclear how much details you would need to understand each line of code. Instead of directly describing the code, could you let us know which lines are unclear?
I would expect that e.g.:

running_loss = 0.0 # initializes the running_loss variable with 0.0

would be clear, but:

with torch.set_grad_enabled(phase == 'train'): # enabled gradient computation if phase=='train' is used

might be more complicated to understand.

i wan to know waht this function do and what’s the output