code: def test_classification_models(arch_name, input_shape):
      batch_size = 8
      model = classification.__dict__[arch_name](
          pretrained=False, input_shape=input_shape
      ).eval()
      assert isinstance(model, torch.nn.Module)
      input_tensor = torch.rand((batch_size, *input_shape))
  
      if torch.cuda.is_available():
          model.cuda()
          input_tensor = input_tensor.cuda()
      out = model(input_tensor)
      assert isinstance(out, torch.Tensor)
      assert out.shape == (8, 4)
  
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 3 for calling a global function with example usage: test_classification_models(arch_name, input_shape) without return types