code: def predict(self, image: np.ndarray, mask: np.ndarray) -> np.ndarray:
      for transform in self.transforms:
          image, mask = transform.transform(image, mask)
      image, mask = image.to(self.device), mask.to(self.device)
      predicted_image = self.net(image, mask)["images"].cpu()
      for transform in reversed(self.transforms):
          predicted_image = transform.inv_transform(predicted_image)
      predicted_image = torch.clamp(predicted_image, 0, 255).to(
          torch.uint8
      )
      return predicted_image.cpu().numpy()
  
file_path: carefree-learn/cflearn/api/cv/third_party/iharm/inference/predictor.py
imports: import torch
  import numpy
  from torch import nn
  from typing import Tuple
  from .transforms import ToTensor
  from .transforms import PadToDivisor
  from .transforms import NormalizeTensor
  from .transforms import AddFlippedTensor
  from ..model.backboned import HRNetIHModel
label_desc: Use image node_id 3 for calling the Predictor obj's underlying member method code with example usage: obj.predict(image, mask) and returns: predicted_image