code: def hinge_embedding_loss(
      input: np.ndarray,
      target: np.ndarray,
      *,
      margin: float = 1.0,
      reduction: str = "mean",
  ) -> np.ndarray:
      zero_ = np.zeros([1], dtype=input.dtype)
  
      relu_part = np.maximum(margin - input, 0)
  
      loss = np.where(target == 1.0, input, zero_) + np.where(
          target == -1.0, relu_part, zero_
      )
  
      return _apply_loss_reduction(loss, reduction)
  
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 7 for calling a global function with example usage: hinge_embedding_loss(input, target) and returns: _apply_loss_reduction