class_docstr: Modification of BiTemperedLogisticLoss for binary classification case.
  It's signature matches nn.BCEWithLogitsLoss: Predictions and target tensors must have shape [B,1,...]
  
  References:
      https://ai.googleblog.com/2019/08/bi-tempered-logistic-loss-for-training.html
      https://arxiv.org/abs/1906.03361
docstr: Args:
      t1:
      t2:
      smoothing:
      ignore_index:
      reduction:
docstr_tok: ["Args",":","t1",":","t2",":","smoothing",":","ignore_index",":","reduction",":"]
code: def __init__(
      self,
      t1: float,
      t2: float,
      smoothing: float = 0.0,
      ignore_index: Optional[int] = None,
      reduction: str = "mean",
  ):
      """
  
      Args:
          t1:
          t2:
          smoothing:
          ignore_index:
          reduction:
      """
      super().__init__()
      self.t1 = t1
      self.t2 = t2
      self.smoothing = smoothing
      self.reduction = reduction
      self.ignore_index = ignore_index
  
imports: from typing import Optional
  import torch
  from torch import nn, Tensor
label_desc: Use image node_id 1 to create a new BinaryBiTemperedLogisticLoss object from inherited base classes: nn with example: obj = BinaryBiTemperedLogisticLoss(t1, t2, smoothing, ignore_index, reduction)