class_docstr: Implementation of https://arxiv.org/abs/1708.01682
  Args:
      alpha: The angle (as described in the paper), specified in degrees.
code: def get_pairs(
      self, embeddings, labels, indices_tuple, ref_emb, ref_labels
  ):
      a1, p, a2, _ = lmu.convert_to_pairs(
          indices_tuple, labels, ref_labels
      )
      if len(a1) == 0 or len(a2) == 0:
          return [None] * 5
      anchors = self.distance.normalize(embeddings[a1])
      positives = self.distance.normalize(ref_emb[p])
      keep_mask = labels[a1].unsqueeze(1) != ref_labels.unsqueeze(0)
      self.set_stats(anchors, positives, embeddings, ref_emb, keep_mask)
      return anchors, positives, keep_mask, a1, p
  
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 3 for calling the AngularLoss obj's underlying member method code with example usage: obj.get_pairs(embeddings, labels, indices_tuple, ref_emb, ref_labels) and returns: anchors,  positives,  keep_mask,  a1,  p, unknown