code: def sr(
      self,
      image: Union[str, Image.Image],
      export_path: Optional[str] = None,
      *,
      max_wh: int = 768,
      clip_range: Optional[Tuple[int, int]] = (0, 1),
  ) -> Tensor:
      res = read_image(image, max_wh, anchor=None)
      # inference
      tensor = torch.from_numpy(res.image)
      tensor = tensor.contiguous().to(self.device)
      if self.use_half:
          tensor = tensor.half()
      with eval_context(self.m):
          output = self.m(tensor).cpu().float()
      if clip_range is not None:
          output = torch.clip(output, *clip_range)
      # handle alpha
      if res.alpha is not None:
          alpha_tensor = torch.from_numpy(res.alpha)
          with torch.no_grad():
              alpha_tensor = F.interpolate(
                  alpha_tensor,
                  output.shape[-2:],
                  mode="nearest",
              )
          output = torch.cat([output, alpha_tensor], dim=1)
      # export
      if export_path is not None:
          save_images(output, export_path)
      self.empty_cuda_cache()
      return output
  
file_path: carefree-learn/cflearn/api/cv/translator.py
imports: import torch
  import torch.nn.functional
  from PIL import Image
  from torch import Tensor
  from typing import Tuple
  from typing import Union
  from typing import Optional
  from cftool.cv import read_image
  from cftool.cv import save_images
  from ..utils import APIMixin
  from ...zoo import DLZoo
  from ...zoo.core import TPipeline
  from ...misc.toolkit import eval_context
label_desc: Use image node_id 1 for calling the TranslatorAPI obj's underlying member method code with example usage: obj.sr(image, export_path) and returns: output