code: def correct(A_hat, label, soft_label, mask):
      # Compute error.
      error = torch.zeros_like(soft_label)
      error[mask] = label[mask] - soft_label[mask]
  
      # Smooth error.
      smoothed_error = label_propagation(A_hat, error)
  
      # Autoscale.
      sigma = error[mask].abs()
      sigma = sigma.sum() / sigma.shape[0]
      scale = sigma / smoothed_error.abs().sum(dim=1, keepdim=True)
      scale[scale.isinf() | (scale > 1000)] = 1.0
  
      # Correct.
      result = soft_label + scale * smoothed_error
      return result
  
file_path: dgl/examples/sparse/c_and_s.py
imports: import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.data import CoraGraphDataset
  from torch.optim import Adam
label_desc: Use image node_id 2 for calling a global function with example usage: correct(A_hat, label, soft_label, mask) and returns: result