code: def hinge_embedding_loss(
      input: TensorLikeType,
      target: TensorLikeType,
      margin: float = 1.0,
      reduction: str = "mean",
  ) -> TensorLikeType:
      # loss_without_reduction = input if y == 1
      #                        = max(0, margin - input) if y == -1
      _check_reduction_value(reduction)
      margin_clamp = torch.clamp_min(margin - input, 0)
      output_margin = torch.where(target != 1, margin_clamp, 0)
      output_self = torch.where(target != -1, input, 0)
      loss = output_margin + output_self
      return _apply_loss_reduction(loss, reduction)
  
imports: import math
  from functools import wraps
  from typing import Callable, Optional, Union
  import torch
  import torch._prims
  import torch._prims_common
  import torch._refs
  from torch._decomp import register_decomposition
  from torch._prims_common import ELEMENTWISE_TYPE_PROMOTION_KIND, NumberType, ShapeType, TensorLike, TensorLikeType
  from torch._prims_common.wrappers import elementwise_type_promotion_wrapper, elementwise_unary_scalar_wrapper, out_wrapper
  from torch._refs import _make_inplace
label_desc: Use image node_id 27 for calling a global function with example usage: hinge_embedding_loss(input, target, margin, reduction) and returns: _apply_loss_reduction