class_docstr: Implementation of https://arxiv.org/abs/1708.01682
  Args:
      alpha: The angle (as described in the paper), specified in degrees.
code: def compute_loss(
      self, embeddings, labels, indices_tuple, ref_emb, ref_labels
  ):
      c_f.labels_required(labels)
      (
          anchors,
          positives,
          keep_mask,
          anchor_idx,
          positive_idx,
      ) = self.get_pairs(
          embeddings, labels, indices_tuple, ref_emb, ref_labels
      )
      if anchors is None:
          return self.zero_losses()
  
      sq_tan_alpha = torch.tan(self.alpha) ** 2
      ap_dot = torch.sum(anchors * positives, dim=1, keepdim=True)
      ap_matmul_embeddings = torch.matmul(
          (anchors + positives), (ref_emb.unsqueeze(2))
      )
      ap_matmul_embeddings = ap_matmul_embeddings.squeeze(2).t()
  
      final_form = (4 * sq_tan_alpha * ap_matmul_embeddings) - (
          2 * (1 + sq_tan_alpha) * ap_dot
      )
      losses = lmu.logsumexp(
          final_form, keep_mask=keep_mask, add_one=True
      )
      return {
          "loss": {
              "losses": losses,
              "indices": (anchor_idx, positive_idx),
              "reduction_type": "pos_pair",
          }
      }
  
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 2 for calling the AngularLoss obj's underlying member method code with example usage: obj.compute_loss(embeddings, labels, indices_tuple, ref_emb, ref_labels) and returns: dict, self