code: def multilabel_margin_loss(
      input, target, size_average=None, reduce=None, reduction="mean"
  ):
      ivy.assertions.check_true(
          input.shape == target.shape,
          lambda: (
              "Same shape is expected for both output and target, but instead got :"
              f" output {input.shape} and target : {target.shape}"
          ),
      )
      input, target = torch_frontend.promote_types_of_torch_inputs(
          input, target
      )
      pos = input[ivy.astype(target, bool)]
      neg = input[ivy.astype(1 - target, bool)]
      loss = ivy.maximum(
          0, 1 - (torch_frontend.unsqueeze(pos, dim=1) - neg)
      )
      reduct = _get_reduction(reduction, size_average, reduce)
      return reduct(loss)
  
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 18 for calling a global function with example usage: multilabel_margin_loss(input, target, size_average, reduce, reduction) and returns: reduct