class_docstr: The Semantic Encoding Loss implementation based on PaddlePaddle.
code: def forward(self, logit, label):
      if logit.ndim == 4:
          logit = logit.squeeze(2).squeeze(3)
      assert (
          logit.ndim == 2
      ), "The shape of logit should be [N, C, 1, 1] or [N, C], but the logit dim is  {}.".format(
          logit.ndim
      )
  
      batch_size, num_classes = paddle.shape(logit)
      se_label = paddle.zeros([batch_size, num_classes])
      for i in range(batch_size):
          hist = paddle.histogram(
              label[i], bins=num_classes, min=0, max=num_classes - 1
          )
          hist = hist.astype("float32") / hist.sum().astype("float32")
          se_label[i] = (hist > 0).astype("float32")
      loss = F.binary_cross_entropy_with_logits(logit, se_label)
      return loss
  
file_path: PaddleSeg/paddleseg/models/losses/semantic_encode_cross_entropy_loss.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 2 for calling the SECrossEntropyLoss obj's underlying member method code with example usage: obj.forward(logit, label) and returns: loss