code: def test_model_predict():
      classes = ["start_tick", "start_gate"]
      path = os.path.dirname(__file__)
      file = os.path.join(path, "static/model.pth")
  
      # Load in a pre-fitted model so it can actually make predictions
      model = Model.load(file, classes)
      image = get_image()
  
      # Test predict method on a single image
      pred = model.predict(image)
      assert isinstance(pred, tuple)
      assert set(pred[0]) == set(classes)
      assert (
          isinstance(pred[1][0], torch.Tensor)
          and pred[1][0].shape[0] == 4
      )
      assert pred[2][0] > 0.5
  
      # Test predict method on a list of images
      preds = model.predict([image])
      assert len(preds) == 1
      assert preds[0][0] == pred[0]
      assert torch.all(preds[0][1] == pred[1])
      assert torch.all(preds[0][2] == pred[2])
  
      # Test predict_top method on a single image
      top_pred = model.predict_top(image)
      assert isinstance(top_pred, tuple)
      assert len(top_pred[0]) == 2
      assert isinstance(top_pred[1], torch.Tensor)
      assert (
          top_pred[1].shape[0] == len(top_pred[0])
          and top_pred[1].shape[1] == 4
      )
      assert top_pred[2][0] > 0.5
  
      # Test predict_top method on a list of images
      top_preds = model.predict_top([image])
      assert len(top_preds) == 1
      assert set(top_preds[0][0]) == set(top_pred[0])
      assert torch.all(
          top_preds[0][1][0] == top_pred[1][0]
      ) or torch.all(top_preds[0][1][0] == top_pred[1][1])
      assert (
          top_preds[0][2][0] == top_pred[2][0]
          or top_preds[0][2][0] == top_pred[2][1]
      )
  
      # Test return values when no predictions are made
      model._model.forward = empty_predictor
      preds = model.predict([image])
      assert len(preds) == 1
      assert (
          preds[0][0] == []
          and preds[0][1].nelement() == 0
          and preds[0][2].nelement() == 0
      )
  
      preds = model.predict_top([image])
      assert len(preds) == 1
      assert (
          preds[0][0] == []
          and preds[0][1].nelement() == 0
          and preds[0][2].nelement() == 0
      )
  
file_path: detecto/detecto/tests/test_core.py
imports: import torch
  from detecto.core import *
  from detecto.utils import read_image
  from .helpers import get_dataset, get_image, get_model, empty_predictor
  from torchvision import transforms
  from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
label_desc: Use image node_id 6 for calling a global function with example usage: test_model_predict() without return types