code: def forward(
      self,
      pos_scores: FloatTensorType,
      neg_scores: FloatTensorType,
      weight: Optional[FloatTensorType],
  ) -> FloatTensorType:
      num_pos = match_shape(pos_scores, -1)
      num_neg = match_shape(neg_scores, num_pos, -1)
  
      # FIXME Workaround for https://github.com/pytorch/pytorch/issues/15223.
      if num_pos == 0 or num_neg == 0:
          return torch.zeros(
              (), device=pos_scores.device, requires_grad=True
          )
  
      if weight is not None:
          match_shape(weight, num_pos)
          loss_per_sample = F.margin_ranking_loss(
              neg_scores,
              pos_scores.unsqueeze(1),
              target=pos_scores.new_full((1, 1), -1, dtype=torch.float),
              margin=self.margin,
              reduction="none",
          )
          loss = (loss_per_sample * weight.unsqueeze(-1)).sum()
      else:
          # more memory efficient way if no weights
          loss = F.margin_ranking_loss(
              neg_scores,
              pos_scores.unsqueeze(1),
              target=pos_scores.new_full((1, 1), -1, dtype=torch.float),
              margin=self.margin,
              reduction="sum",
          )
  
      return loss
  
imports: from abc import ABC, abstractmethod
  from typing import Optional
  import torch
  from torch import nn
  from torch.nn import functional
  from torchbiggraph.model import match_shape
  from torchbiggraph.plugin import PluginRegistry
  from torchbiggraph.types import FloatTensorType
label_desc: Use image node_id 2 for calling the RankingLossFunction obj's underlying member method code with example usage: obj.forward(pos_scores, neg_scores, weight) and returns: loss, torch