code: def fitting_loss(sq_distance, radius):
      """KPConv fitting loss. For each query point it ensures that at least one neighboor is
      close to each kernel point
  
      Arguments:
          sq_distance - For each querry point, from all neighboors to all KP points [N_querry, N_neighboors, N_KPoints]
          radius - Radius of the convolution
      """
      kpmin = sq_distance.min(dim=1)[0]
      normalised_kpmin = kpmin / (radius**2)
      return torch.mean(normalised_kpmin)
  
imports: import torch
label_desc: Use image node_id 1 for calling a global function with example usage: fitting_loss(sq_distance, radius) and returns: torch