class_docstr: L2 norm of the gradient estimated as the average change of a field value f
  accross neighbouring points within a radius r
class_docstr_tok: ["L2","norm","of","the","gradient","estimated","as","the","average","change","of","a","field","value","f","accross","neighbouring","points","within","a","radius","r"]
code: def __init__(self, r, aggr=torch.mean):
      super().__init__()
      self._r = r
      self._aggr = aggr
  
file_path: torch-points3d/torch_points3d/core/losses/dirichlet_loss.py
imports: import torch
  import torch_points_kernels
  from torch_cluster import radius
  from torch_scatter import scatter_add
label_desc: Use image node_id 1 to create a new DirichletLoss object from inherited base classes: torch.nn with example: obj = DirichletLoss(r, aggr)