code: def margin_ranking_loss(
      input1: TensorLikeType,
      input2: TensorLikeType,
      target: TensorLikeType,
      margin: float = 0.0,
      reduction: str = "mean",
  ) -> TensorLikeType:
      # loss_without_reduction = max(0, −target * (input1 − input2) + margin)
      if input1.ndim != input2.ndim or input1.ndim != target.ndim:
          raise RuntimeError(
              "margin_ranking_loss : All input tensors should have same dimension but got sizes: "
              f"input1: {input1.shape}, input2: {input2.shape}, target: {target.shape} "
          )
      _check_reduction_value(reduction)
      loss = torch.clamp_min(-target * (input1 - input2) + margin, 0)
      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 25 for calling a global function with example usage: margin_ranking_loss(input1, input2, target, margin, reduction) and returns: _apply_loss_reduction