class_docstr: Implementation of https://arxiv.org/abs/1708.01682
  Args:
      alpha: The angle (as described in the paper), specified in degrees.
code: def set_stats(
      self, anchors, positives, embeddings, ref_emb, keep_mask
  ):
      if self.collect_stats:
          with torch.no_grad():
              centers = (anchors + positives) / 2
              ap_dist = self.distance.pairwise_distance(
                  anchors, positives
              )
              nc_dist = self.distance.get_norm(
                  centers - ref_emb.unsqueeze(1), dim=2
              ).t()
              angles = torch.atan(ap_dist.unsqueeze(1) / (2 * nc_dist))
              average_angle = torch.sum(angles[keep_mask]) / torch.sum(
                  keep_mask
              )
              self.average_angle = np.degrees(average_angle.item())
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/angular_loss.py
imports: import numpy
  import torch
  from ..distances import LpDistance
  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 4 for calling the AngularLoss obj's underlying member method code with example usage: obj.set_stats(anchors, positives, embeddings, ref_emb, keep_mask) without return types