text: import numpy as np
  import torch
  from torch.autograd import Variable
  from pytorch2keras.converter import pytorch_to_keras
  import torchvision
  
  if __name__ == '__main__':
      max_error = 0
      for i in range(10):
          model = torchvision.models.AlexNet()
          model.eval()
  
          input_np = np.random.uniform(0, 1, (1, 3, 224, 224))
          input_var = Variable(torch.FloatTensor(input_np))
          output = model(input_var)
  
          k_model = pytorch_to_keras(model, input_var, (3, 224, 224,), verbose=True)
  
          pytorch_output = output.data.numpy()
          keras_output = k_model.predict(input_np)
  
          error = np.max(pytorch_output - keras_output)
          print(error)
          if max_error < error:
              max_error = error
  
      print('Max error: {0}'.format(max_error))
  