class_docstr: Cross entropy loss for 2.5D parallelism
  
  Args:
      reduction (bool, optional): whether to average the loss, defaults to True.
  
  The ``args`` and ``kwargs`` should include parameters below:
  ::
  
      weight (Tensor, optional)
      size_average (bool, optional)
      ignore_index (int, optional)
      reduce (bool, optional)
      label_smoothing (float, optional)
  
  More details about ``args``, ``kwargs`` and ``torch.nn.functional.cross_entropy`` could be found in
  `Cross_entropy <https://pytorch.org/docs/stable/generated/torch.nn.functional.cross_entropy.html#torch.nn.functional.cross_entropy>`_.
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_2p5d(targets)
      loss = cross_entropy(
          logits,
          targets,
          reduction="none",
          *self.loss_args,
          **self.loss_kwargs,
      )
      if self.reduction_mean:
          loss = loss.mean()
          loss = reduce_by_batch_2p5d(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_2p5d import reduce_by_batch_2p5d, split_batch_2p5d
  from colossalai.legacy.nn.layer.parallel_2p5d._utils import assert_tesseract_initialization
  from colossalai.legacy.registry import LOSSES
  from colossalai.utils import get_current_device
label_desc: Use image node_id 2 for calling the CrossEntropyLoss2p5D obj's underlying member method code with example usage: obj.forward(logits, targets) and returns: loss