class_docstr: The implementation of distill cross entropy loss.
  
  Args:
      weight (tuple|list|ndarray|Tensor, optional): A manual rescaling weight
          given to each class. Its length must be equal to the number of classes.
          Default ``None``.
      ignore_index (int64, optional): Specifies a target value that is ignored
          and does not contribute to the input gradient. Default ``255``.
      top_k_percent_pixels (float, optional): the value lies in [0.0, 1.0].
          When its value < 1.0, only compute the loss for the top k percent pixels
          (e.g., the top 20% pixels). This is useful for hard pixel mining.
          Default ``1.0``.
      data_format (str, optional): The tensor format to use, 'NCHW' or 'NHWC'.
          Default ``'NCHW'``.
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code: def __init__(
      self,
      weight=None,
      ignore_index=255,
      top_k_percent_pixels=1.0,
      data_format="NCHW",
  ):
      super().__init__(
          weight, ignore_index, top_k_percent_pixels, data_format
      )
  
file_path: PaddleSeg/paddleseg/models/losses/cross_entropy_loss.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 DistillCrossEntropyLoss object from inherited base classes: CrossEntropyLoss with example: obj = DistillCrossEntropyLoss(weight, ignore_index, top_k_percent_pixels, data_format)