code: def soft_margin_loss(
      input: np.ndarray,
      target: np.ndarray,
      /,
      *,
      reduction: Optional[str] = "mean",
  ) -> np.ndarray:
      loss = np.sum(np.log1p(np.exp(-input * target))) / input.size
  
      if reduction == "mean":
          return np.mean(loss)
      elif reduction == "sum":
          return np.sum(loss)
      else:
          return loss
  
file_path: ivy/ivy/functional/backends/numpy/experimental/losses.py
imports: import numpy
  from typing import Optional
  from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_device_and_dtypes
  from .None import backend_version
label_desc: Use image node_id 3 for calling a global function with example usage: soft_margin_loss() and returns: np, np, loss