code: def __init__(self, num_classes, embedding_size, **kwargs):
      super().__init__(**kwargs)
      self.proxies = torch.nn.Parameter(
          torch.Tensor(num_classes, embedding_size)
      )
      self.weight_init_func(self.proxies)
      self.proxy_labels = torch.arange(num_classes)
      self.add_to_recordable_attributes(
          list_of_names=["num_classes"], is_stat=False
      )
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/proxy_losses.py
imports: import torch
  from ..utils import common_functions
  from .mixins import WeightRegularizerMixin
  from .nca_loss import NCALoss
label_desc: Use image node_id 1 to create a new ProxyNCALoss object from inherited base classes: WeightRegularizerMixin, NCALoss with example: obj = ProxyNCALoss(num_classes, embedding_size)