code: def test_pytorch_identical_output():
      import torch  # pylint: disable=import-outside-toplevel
  
      weights_path_torch = keras_ocr.tools.download_and_verify(
          url="https://github.com/faustomorales/keras-ocr/releases/download/v0.8.4/craft_mlt_25k.pth",
          filename="craft_mlt_25k.pth",
          sha256="4a5efbfb48b4081100544e75e1e2b57f8de3d84f213004b14b85fd4b3748db17",
      )
      weights_path_keras = keras_ocr.tools.download_and_verify(
          url="https://github.com/faustomorales/keras-ocr/releases/download/v0.8.4/craft_mlt_25k.h5",
          filename="craft_mlt_25k.h5",
          sha256="7283ce2ff05a0617e9740c316175ff3bacdd7215dbdf1a726890d5099431f899",
      )
  
      model_keras = keras_ocr.detection.build_keras_model(
          weights_path=weights_path_keras
      )
      model_pytorch = keras_ocr.detection.build_torch_model(
          weights_path=weights_path_torch
      )
      image = keras_ocr.tools.read("tests/test_image.jpg")
      X = keras_ocr.detection.compute_input(image)[np.newaxis,]
      y_pred_keras = model_keras.predict(X)
      y_pred_torch = (
          model_pytorch.forward(
              torch.from_numpy(X.transpose(0, 3, 1, 2))
          )[0]
          .detach()
          .numpy()
      )
      np.testing.assert_almost_equal(
          y_pred_keras, y_pred_torch, decimal=4
      )
  
imports: import os
  import numpy
  import pytest
  import keras_ocr
label_desc: Use image node_id 1 for calling a global function with example usage: test_pytorch_identical_output() without return types