class_docstr: Implements the cross entropy loss function.
  
  Args:
      min_K (int): the minimum number of pixels to be counted in loss computation.
      loss_th (float): the loss threshold. Only loss that is larger than the threshold
          would be calculated.
      weight (tuple|list, optional): The weight for different classes. Default: None.
      ignore_index (int, optional): Specifies a target value that is ignored
          and does not contribute to the input gradient. Default: 255.
code: def __init__(self, min_K, loss_th, weight=None, ignore_index=255):
      super().__init__()
      self.ignore_index = ignore_index
      self.K = min_K
      self.threshold = loss_th
      if weight is not None:
          weight = paddle.to_tensor(weight, dtype="float32")
      self.weight = weight
  
file_path: PaddleSeg/paddleseg/models/losses/bootstrapped_cross_entropy.py
imports: import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 1 to create a new BootstrappedCrossEntropyLoss object from inherited base classes: nn with example: obj = BootstrappedCrossEntropyLoss(min_K, loss_th, weight, ignore_index)