code: def forward(self, x):
      with torch.no_grad():
          prediction = self.model(x)
      return prediction
  
imports: import math
  import torch
  import numpy
  import torch.nn
  from torchvision.transforms import Compose
  from .core.dpt_depth import DPTDepthModel
  from .core.midas_net import MidasNet
  from .core.midas_net_custom import MidasNet_small
  from .core.transforms import Resize, NormalizeImage, PrepareForNet
  from .....misc.toolkit import download_model
label_desc: Use image node_id 2 for calling the MiDaSInference obj's underlying member method code with example usage: obj.forward(x) and returns: prediction