code: def test_classification_zoo(arch_name):
      batch_size = 16
      # Model
      predictor = classification.zoo.crop_orientation_predictor(
          arch_name, pretrained=False
      )
      predictor.model.eval()
  
      with pytest.raises(ValueError):
          predictor = classification.zoo.crop_orientation_predictor(
              arch="wrong_model", pretrained=False
          )
      # object check
      assert isinstance(predictor, CropOrientationPredictor)
      input_tensor = torch.rand((batch_size, 3, 128, 128))
      if torch.cuda.is_available():
          predictor.model.cuda()
          input_tensor = input_tensor.cuda()
  
      with torch.no_grad():
          out = predictor(input_tensor)
      out = predictor(input_tensor)
      assert isinstance(out, list) and len(out) == batch_size
      assert all(isinstance(pred, int) for pred in out)
  
file_path: doctr/tests/pytorch/test_models_classification_pt.py
imports: import os
  import tempfile
  import cv2
  import numpy
  import onnxruntime
  import pytest
  import torch
  from doctr.models import classification
  from doctr.models.classification.predictor import CropOrientationPredictor
  from doctr.models.utils import export_model_to_onnx
label_desc: Use image node_id 4 for calling a global function with example usage: test_classification_zoo(arch_name) without return types