code: def __init__(
      self,
      normalize_embeddings=True,
      p=2,
      power=1,
      is_inverted=False,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.normalize_embeddings = normalize_embeddings
      self.p = p
      self.power = power
      self.is_inverted = is_inverted
      self.add_to_recordable_attributes(
          list_of_names=["p", "power"], is_stat=False
      )
      self.add_to_recordable_attributes(
          list_of_names=[
              "initial_avg_query_norm",
              "initial_avg_ref_norm",
              "final_avg_query_norm",
              "final_avg_ref_norm",
          ],
          is_stat=True,
      )
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/distances/base_distance.py
imports: import torch
  from ..utils.module_with_records import ModuleWithRecords
label_desc: Use image node_id 1 to create a new BaseDistance object from inherited base classes: ModuleWithRecords with example: obj = BaseDistance(normalize_embeddings, p, power, is_inverted)