code: def _triplet_margin_with_distance_loss(
      anchor: TensorLikeType,
      positive: TensorLikeType,
      negative: TensorLikeType,
      *,
      distance_function: Optional[
          Callable[[TensorLikeType, TensorLikeType], TensorLikeType]
      ] = None,
      margin: float = 1.0,
      swap: bool = False,
      reduction: str = "mean",
  ) -> TensorLikeType:
      _check_reduction_value(reduction)
  
      a_dim = anchor.ndim
      p_dim = positive.ndim
      n_dim = negative.ndim
      torch._check(
          a_dim == p_dim and p_dim == n_dim,
          lambda: (
              f"The anchor, positive, and negative tensors are expected to have "
              f"the same number of dimensions, but got: anchor {a_dim}D, "
              f"positive {p_dim}D, and negative {n_dim}D inputs"
          ),
      )
  
      if distance_function is None:
          distance_function = torch.pairwise_distance
  
      dist_pos = distance_function(anchor, positive)
      dist_neg = distance_function(anchor, negative)
      # The distance swap is described in the paper "Learning shallow
      # convolutional feature descriptors with triplet losses" by V. Balntas, E.
      # Riba et al.  If True, and if the positive example is closer to the
      # negative example than the anchor is, swaps the positive example and the
      # anchor in the loss computation.
      if swap:
          dist_swap = distance_function(positive, negative)
          dist_neg = torch.minimum(dist_neg, dist_swap)
      loss = torch.clamp_min(margin + dist_pos - dist_neg, 0)
      return _apply_loss_reduction(loss, reduction)
  
imports: import math
  from functools import wraps
  from typing import Callable, Optional, Union
  import torch
  import torch._prims
  import torch._prims_common
  import torch._refs
  from torch._decomp import register_decomposition
  from torch._prims_common import ELEMENTWISE_TYPE_PROMOTION_KIND, NumberType, ShapeType, TensorLike, TensorLikeType
  from torch._prims_common.wrappers import elementwise_type_promotion_wrapper, elementwise_unary_scalar_wrapper, out_wrapper
  from torch._refs import _make_inplace
label_desc: Use image node_id 34 for calling a global function with example usage: _triplet_margin_with_distance_loss(anchor, positive, negative) and returns: _apply_loss_reduction