code: def __init__(self, b=2, alpha=1, anneal=0.01, variant="O", **kwargs):
      super().__init__(**kwargs)
      c_f.assert_distance_type(self, CosineSimilarity)
      self.b = b
      self.alpha = alpha
      self.anneal = anneal
      self.variant = variant
      if self.variant not in self.VARIANTS:
          raise ValueError(
              f"variant={variant} but must be one of {self.VARIANTS}"
          )
  
      """
          Adapted from https://github.com/interestingzhuo/PNPloss
          """
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/pnp_loss.py
imports: import torch
  from ..distances import CosineSimilarity
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .base_metric_loss_function import BaseMetricLossFunction
label_desc: Use image node_id 1 to create a new PNPLoss object from inherited base classes: BaseMetricLossFunction with example: obj = PNPLoss(b, alpha, anneal, variant)