class_docstr: This class defines a custom loss function for log confidence.
  
  Attributes:
  aux_coef: A float indicating the auxiliary coefficient.
  warmup_frac: A float indicating the fraction of total training steps for warmup.
class_docstr_tok: ["This","class","defines","a","custom","loss","function","for","log","confidence",".","Attributes",":","aux_coef",":","A","float","indicating","the","auxiliary","coefficient",".","warmup_frac",":","A","float","indicating","the","fraction","of","total","training","steps","for","warmup","."]
code: def __init__(
      self,
      aux_coef: float = 0.5,
      warmup_frac: float = 0.1,  # in terms of fraction of total training steps
  ):
      self.aux_coef = aux_coef
      self.warmup_frac = warmup_frac
  
file_path: weak-to-strong/weak_to_strong/loss.py
imports: import torch
label_desc: Use image node_id 1 to create a new logconf_loss_fn object from inherited base classes: LossFnBase with example: obj = logconf_loss_fn(aux_coef, warmup_frac)