code: def segment(input):
      inp = input
      x = inp.transpose([2, 0, 1])  #  channels-first
      x = np.expand_dims(x, axis=0)  # adding a batch dimension
  
      mean = x.mean(axis=(0, 2, 3))
      std = x.std(axis=(0, 2, 3))
      x = x - mean.reshape(1, 3, 1, 1)
      x = x / std.reshape(1, 3, 1, 1)
  
      with torch.no_grad():
          x = torch.from_numpy(x).type("torch.FloatTensor").to(device)
          output = model(x)
  
      y = output["out"].numpy()
      y = y.squeeze()
  
      out = y > 0
  
      mask = inp.copy()
      mask[out] = np.array([0, 0, 255])
  
      return mask
  
imports: import os
  import numpy
  import torch
  import torchvision
  import wget
  import gradio
label_desc: Use image node_id 2 for calling a global function with example usage: segment(input) and returns: mask