code: def __init__(self, cutoff=0.5, nonzero_loss_cutoff=1.4, **kwargs):
      super().__init__(**kwargs)
      c_f.assert_distance_type(
          self, LpDistance, p=2, power=1, normalize_embeddings=True
      )
      self.cutoff = float(cutoff)
      self.nonzero_loss_cutoff = float(nonzero_loss_cutoff)
      self.add_to_recordable_attributes(
          list_of_names=["cutoff", "nonzero_loss_cutoff"], is_stat=False
      )
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/miners/distance_weighted_miner.py
imports: import torch
  from ..distances import LpDistance
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .base_miner import BaseMiner
label_desc: Use image node_id 1 to create a new DistanceWeightedMiner object from inherited base classes: BaseMiner with example: obj = DistanceWeightedMiner(cutoff, nonzero_loss_cutoff)