class_docstr: Balanced binary cross-entropy loss.
  
  https://arxiv.org/pdf/1504.06375.pdf (Formula 2)
docstr: Args:
      gamma:
      ignore_index:
      reduction:
docstr_tok: ["Args",":","gamma",":","ignore_index",":","reduction",":"]
code: def __init__(
      self,
      gamma: float = 1.0,
      reduction="mean",
      ignore_index: Optional[int] = None,
  ):
      """
  
      Args:
          gamma:
          ignore_index:
          reduction:
      """
      super().__init__()
      self.gamma = gamma
      self.reduction = reduction
      self.ignore_index = ignore_index
  
file_path: pytorch-toolbelt/pytorch_toolbelt/losses/balanced_bce.py
imports: from typing import Optional
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
label_desc: Use image node_id 1 to create a new BalancedBCEWithLogitsLoss object from inherited base classes: nn with example: obj = BalancedBCEWithLogitsLoss(gamma, reduction, ignore_index)