code: def triplet_loss(x_1, y, x_2, z, loss_type="margin", margin=1.0):
      """Compute triplet loss.
      This function computes loss on a triplet of inputs (x, y, z).  A similarity or
      distance value is computed for each pair of (x, y) and (x, z).  Since the
      representations for x can be different in the two pairs (like our matching
      model) we distinguish the two x representations by x_1 and x_2.
      Args:
        x_1: [N, D] float tensor.
        y: [N, D] float tensor.
        x_2: [N, D] float tensor.
        z: [N, D] float tensor.
        loss_type: margin or hamming.
        margin: float scalar, margin for the margin loss.
      Returns:
        loss: [N] float tensor.  Loss for each pair of representations.
      """
      if loss_type == "margin":
          return torch.relu(
              margin
              + euclidean_distance(x_1, y)
              - euclidean_distance(x_2, z)
          )
      elif loss_type == "hamming":
          return 0.125 * (
              (approximate_hamming_similarity(x_1, y) - 1) ** 2
              + (approximate_hamming_similarity(x_2, z) + 1) ** 2
          )
      else:
          raise ValueError("Unknown loss_type %s" % loss_type)
  
file_path: DIG/dig/auggraph/method/SMixup/utils/utils.py
imports: from torch_geometric.utils import degree
  import torch
label_desc: Use image node_id 3 for calling a global function with example usage: triplet_loss(x_1, y, x_2, z, loss_type, margin) and returns: torch, unknown