code: def soft_margin_loss(
      input: JaxArray,
      target: JaxArray,
      /,
      *,
      reduction: Optional[str] = "mean",
  ) -> JaxArray:
      loss = jnp.sum(jnp.log1p(jnp.exp(-input * target))) / jnp.size(
          input
      )
  
      if reduction == "mean":
          return jnp.mean(loss)
      elif reduction == "sum":
          return jnp.sum(loss)
      else:
          return loss
  
file_path: ivy/ivy/functional/backends/jax/experimental/losses.py
imports: import jax.numpy
  from typing import Optional
  from ivy.functional.backends.jax import JaxArray
  from ivy.func_wrapper import 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: jnp, jnp, loss