## Quick-Start Example ```python import torch from torchvision import models import numpy as np import matplotlib from PIL import Image import importlib ms_model = importlib.import_module("multi-scale-expansion.model") ms_datasets = importlib.import_module("multi-scale-expansion.dataset") ms = importlib.import_module("multi-scale-expansion.classification") device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") mock_model = models.resnet18(weights='DEFAULT') mock_model = ms_model.get_plant_model(mock_model, list(range(6))) mock_lr = 0.001 mock_momentum = 0.9 mock_step_size = 7 mock_gamma = 0.1 mock_criterion, mock_optimizer, mock_lr_scheduler = get_train_loss_needs( mock_model, mock_lr, mock_momentum, mock_step_size, mock_gamma ) mock_datasets = { "train": FakeData(num_classes=6, transform=mock_transforms["train"]), "test": FakeData(num_classes=6, transform=mock_transforms["test"]), } mock_dataset_sizes = {x: len(mock_datasets[x]) for x in ['train', 'test']} mock_dataloaders = ms_datasets.get_dataloaders(mock_datasets) model, train_losses, train_accuracies, val_losses, val_accuracies = ms.train_model( device, mock_dataset_sizes, mock_dataloaders, mock_model, mock_criterion, mock_optimizer, mock_lr_scheduler, num_epochs=1, testing=True, ) ``` And now your model is ready-to-use!