code: def test_classification_architectures(
      arch_name, input_shape, output_size
  ):
      # Model
      batch_size = 2
      tf.keras.backend.clear_session()
      model = classification.__dict__[arch_name](
          pretrained=True, include_top=True, input_shape=input_shape
      )
      # Forward
      out = model(
          tf.random.uniform(
              shape=[batch_size, *input_shape],
              maxval=1,
              dtype=tf.float32,
          )
      )
      # Output checks
      assert isinstance(out, tf.Tensor)
      assert out.dtype == tf.float32
      assert out.numpy().shape == (batch_size, *output_size)
  
file_path: doctr/tests/tensorflow/test_models_classification_tf.py
imports: import os
  import tempfile
  import cv2
  import numpy
  import onnxruntime
  import psutil
  import pytest
  import tensorflow
  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 1 for calling a global function with example usage: test_classification_architectures(arch_name, input_shape, output_size) without return types