class_docstr: Vocab parallel cross entropy loss for 2D parallelism.
  
  Args:
      reduction (bool, optional): whether to average the loss, defaults to True.
docstr: Calculate loss between logits and targets.
  
  Args:
      logits (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
      targets (:class:`torch.tensor`): Ground truth class indices or class probabilities.
docstr_tok: ["Calculate","loss","between","logits","and","targets",".","Args",":","logits","(",":","class",":","`","torch.tensor","`",")",":","Predicted","unnormalized","scores","(","often","referred","to","as","logits",")",".","targets","(",":","class",":","`","torch.tensor","`",")",":","Ground","truth","class","indices","or","class","probabilities","."]
code: def forward(self, logits, targets):
      """Calculate loss between logits and targets.
  
      Args:
          logits (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
          targets (:class:`torch.tensor`): Ground truth class indices or class probabilities.
      """
      targets = split_batch_2d(targets)
      loss = _VocabParallelCrossEntropy2D.apply(
          logits,
          targets,
      )
      if self.reduction_mean:
          loss = loss.mean()
          loss = reduce_by_batch_2d(loss, True)
      return loss
  
imports: import torch
  import torch.distributed
  from torch.cuda.amp import custom_bwd, custom_fwd
  from torch.nn.functional import cross_entropy
  from torch.nn.modules.loss import _Loss
  from colossalai.legacy.context import ParallelMode
  from colossalai.legacy.core import global_context
  from colossalai.legacy.nn.layer.parallel_2d import reduce_by_batch_2d, split_batch_2d
  from colossalai.legacy.nn.layer.parallel_2d._utils import assert_summa_initialization
  from colossalai.legacy.registry import LOSSES
  from colossalai.utils import get_current_device
label_desc: Use image node_id 2 for calling the VocabParallelCrossEntropyLoss2D obj's underlying member method code with example usage: obj.forward(logits, targets) and returns: loss