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","."]
code: def __init__(
      self,
      proj_weight: nn.Parameter,
      tile_factor: int = 0,
      log_softmax: bool = True,
      margin: float = 0.35,
      scale: Optional[float] = None,
  ):
      super().__init__(
          proj_weight, tile_factor, log_softmax, margin, scale
      )
  
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 1 to create a new BaselineSoftmaxNllLoss object from inherited base classes: BaselineSoftmax with example: obj = BaselineSoftmaxNllLoss(proj_weight, tile_factor, log_softmax, margin, scale)