code: def triplet_margin_loss(
      input,
      positive,
      negative,
      margin=1.0,
      p=2.0,
      eps=1e-06,
      swap=False,
      reduction="mean",
  ):
      reduction = _get_reduction_func(reduction)
  
      a_dim = input.ndim
      p_dim = positive.ndim
      n_dim = negative.ndim
  
      ivy.assertions.check_true(
          a_dim == p_dim and p_dim == n_dim,
          lambda: (
              "The input, positive, and negative tensors are expected to have "
              f"the same number of dimensions, but got: input {a_dim}D, "
              f"positive {p_dim}D, and negative {n_dim}D inputs"
          ),
      )
  
      dist_positive = _pairwise_distance(input, positive, p=p, eps=eps)
      dist_negative = _pairwise_distance(input, negative, p=p, eps=eps)
      if swap:
          dist_swap = _pairwise_distance(
              positive, negative, p=p, eps=eps
          )
          dist_negative = ivy.minimum(dist_negative, dist_swap)
      loss = ivy.maximum(
          dist_positive - dist_negative + ivy.array(margin),
          ivy.array(0.0),
      )
  
      loss = reduction(loss).astype(input.dtype)
      return loss
  
imports: import ivy
  from ivy.func_wrapper import with_supported_dtypes
  import ivy.functional.frontends.paddle
  from ivy.utils.exceptions import handle_exceptions
  from ivy.functional.frontends.paddle.func_wrapper import inputs_to_ivy_arrays, to_ivy_arrays_and_back
label_desc: Use image node_id 19 for calling a global function with example usage: triplet_margin_loss(input, positive, negative, margin, p, eps, swap, reduction) and returns: loss