code: def triplet_margin_with_distance_loss(
      anchor,
      positive,
      negative,
      distance_function=None,
      margin=1.0,
      swap=False,
      reduction="mean",
  ):
      reduction = _get_reduction(reduction)
  
      a_dim = anchor.ndim
      p_dim = positive.ndim
      n_dim = negative.ndim
  
      ivy.assertions.check_true(
          a_dim == p_dim and p_dim == n_dim,
          lambda: (
              "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 = pairwise_distance
  
      dist_pos = distance_function(anchor, positive)
      dist_neg = distance_function(anchor, negative)
      if swap:
          dist_swap = distance_function(positive, negative)
          dist_neg = ivy.minimum(dist_neg, dist_swap)
  
      loss = ivy.maximum(
          dist_pos - dist_neg + ivy.array(margin), ivy.array(0.0)
      )
  
      return reduction(loss).astype(anchor.dtype)
  
imports: import ivy
  import ivy.functional.frontends.torch
  from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
label_desc: Use image node_id 27 for calling a global function with example usage: triplet_margin_with_distance_loss(anchor, positive, negative, distance_function, margin, swap, reduction) and returns: reduction