class_docstr: Implements the cross entropy loss function. It only compute the edge part.
  
  Args:
      edge_threshold (float, optional): The pixels greater edge_threshold as edges. Default: 0.8.
      thresh (float, optional): The threshold of ohem. Default: 0.7.
      min_kept (int, optional): The min number to keep in loss computation. Default: 5000.
      ignore_index (int64, optional): Specifies a target value that is ignored
          and does not contribute to the input gradient. Default ``255``.
code: def __init__(
      self,
      edge_threshold=0.8,
      thresh=0.7,
      min_kept=5000,
      ignore_index=255,
  ):
      super().__init__()
      self.edge_threshold = edge_threshold
      self.thresh = thresh
      self.min_kept = min_kept
      self.ignore_index = ignore_index
      self.EPS = 1e-10
  
file_path: PaddleSeg/paddleseg/models/losses/ohem_edge_attention_loss.py
imports: import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import losses
label_desc: Use image node_id 1 to create a new OhemEdgeAttentionLoss object from inherited base classes: nn with example: obj = OhemEdgeAttentionLoss(edge_threshold, thresh, min_kept, ignore_index)