code: def compute_loss(self, embeddings):
      reg = torch.norm(embeddings, p=self.p, dim=1)
      if self.power != 1:
          reg = reg**self.power
      return {
          "loss": {
              "losses": reg,
              "indices": c_f.torch_arange_from_size(embeddings),
              "reduction_type": "element",
          }
      }
  
imports: import torch
  from ..utils import common_functions
  from .base_regularizer import BaseRegularizer
label_desc: Use image node_id 2 for calling the LpRegularizer obj's underlying member method code with example usage: obj.compute_loss(embeddings) and returns: dict