code: def forward(self, X, target):
      loss = self.loss(X, target)
      if self.ignore_index >= 0:
          ignored_positions = target == self.ignore_index
          size = (target.size(0) - ignored_positions.sum()).item()
          loss.masked_fill_(ignored_positions, 0.0)
      else:
          size = target.size(0)
      if self.reduction == "sum":
          loss = loss.sum()
      elif self.reduction == "elementwise_mean":
          if size == 0:
              # Returns zero loss and zero gradient in the rare case that all row targets are ignored.
              loss = loss.sum() * 0.0
          else:
              loss = loss.sum() / float(size)
      return loss
  
imports: import torch
  import torch.nn
  from torch.autograd import Function
  from ludwig.constants import IGNORE_INDEX_TOKEN_ID
  from ludwig.utils.entmax.activations import entmax15, sparsemax
  from ludwig.utils.entmax.root_finding import entmax_bisect, sparsemax_bisect
label_desc: Use image node_id 2 for calling the _GenericLoss obj's underlying member method code with example usage: obj.forward(X, target) and returns: loss