code: def classify(self, img):
      img = img.float() / 255
      mean = torch.tensor([[[0.485, 0.456, 0.406]]]).float()
      std = torch.tensor([[[0.229, 0.224, 0.225]]]).float()
      img = (img - mean) / std
      img = img.permute(0, 3, 2, 1)
      out = self.model(img)
  
      method_res = self.method_from_exposed(42)
      assert method_res == ("method", 42)
      func_res = _func_from_exposed("DouglasAdams")
      assert func_res == ("func", "DouglasAdams")
  
      return out.argmax()
  
imports: from pathlib import Path
  import pytorch_lightning
  import torch
  from flash.core.serve import ModelComponent, expose
  from flash.core.serve.types import Image, Label, Number, Repeated
  from flash.core.utilities.imports import _TORCHVISION_AVAILABLE
  from torch import Tensor
label_desc: Use image node_id 2 for calling the ClassificationInference obj's underlying member method code with example usage: obj.classify(img) and returns: out