code: def _apply_loss_reduction(
      loss: np.ndarray, reduction: str
  ) -> np.ndarray:
      if reduction == "sum":
          return np.sum(loss)
      elif reduction == "mean":
          return np.mean(loss)
      else:  # reduction == "none"
          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 4 for calling a global function with example usage: _apply_loss_reduction(loss, reduction) and returns: np, np, loss