class_docstr: Baseline that does an output projection, a softmax & a NLL loss (cross-entropy).
  
  See BaselineSoftmax above. Constructor is the same. Only difference is in the
  forward function.
  
  This class is used for testing and benchmarking.
class_docstr_tok: ["Baseline","that","does","an","output","projection",",","a","softmax","&","a","NLL","loss","(","cross-entropy",")",".","See","BaselineSoftmax","above",".","Constructor","is","the","same",".","Only","difference","is","in","the","forward","function",".","This","class","is","used","for","testing","and","benchmarking","."]
docstr: Forward that directly compute the loss.
docstr_tok: ["Forward","that","directly","compute","the","loss","."]
code: def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:  # type: ignore
      """Forward that directly compute the loss."""
      assert isinstance(input, torch.Tensor)
      assert isinstance(target, torch.Tensor)
      input, target = _reshape_inputs(input, target)
      x = super().forward(input, target)
      return F.nll_loss(x, target, reduction="sum")
  
imports: from typing import Any, Optional, Tuple
  import torch
  from torch import nn
  import torch.distributed
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the BaselineSoftmaxNllLoss obj's underlying member method code with example usage: obj.forward(input, target) and returns: F