code: def repulsion_loss(deformed_kpoints, radius):
      """Ensures that the deformed points within the kernel remain equidistant
  
      Arguments:
          deformed_kpoints - deformed points for each query point
          radius - Radius of the kernel
      """
      deformed_kpoints / float(radius)
      n_points = deformed_kpoints.shape[1]
      repulsive_loss = 0
      for i in range(n_points):
          with torch.no_grad():
              other_points = torch.cat(
                  [
                      deformed_kpoints[:, :i, :],
                      deformed_kpoints[:, i + 1 :, :],
                  ],
                  dim=1,
              )
          distances = torch.sqrt(
              torch.sum(
                  (other_points - deformed_kpoints[:, i : i + 1, :])
                  ** 2,
                  dim=-1,
              )
          )
          repulsion_force = torch.sum(
              torch.pow(torch.relu(1.5 - distances), 2), dim=1
          )
          repulsive_loss += torch.mean(repulsion_force)
      return repulsive_loss
  
imports: import torch
label_desc: Use image node_id 2 for calling a global function with example usage: repulsion_loss(deformed_kpoints, radius) and returns: repulsive_loss