class_docstr: The class for implementing FCENet head.
  FCENet(CVPR2021): Fourier Contour Embedding for Arbitrary-shaped Text
  Detection.
  
  [https://arxiv.org/abs/2104.10442]
  
  Args:
      in_channels (int): The number of input channels.
      scales (list[int]) : The scale of each layer.
      fourier_degree (int) : The maximum Fourier transform degree k.
code: def forward(self, feats, targets=None):
      cls_res, reg_res = multi_apply(self.forward_single, feats)
      level_num = len(cls_res)
      outs = {}
      if not self.training:
          for i in range(level_num):
              tr_pred = F.softmax(cls_res[i][:, 0:2, :, :], axis=1)
              tcl_pred = F.softmax(cls_res[i][:, 2:, :, :], axis=1)
              outs["level_{}".format(i)] = paddle.concat(
                  [tr_pred, tcl_pred, reg_res[i]], axis=1
              )
      else:
          preds = [[cls_res[i], reg_res[i]] for i in range(level_num)]
          outs["levels"] = preds
      return outs
  
file_path: PaddleOCR/ppocr/modeling/heads/det_fce_head.py
imports: from paddle import nn
  from paddle import ParamAttr
  import paddle.nn.functional
  from paddle.nn.initializer import Normal
  import paddle
  from functools import partial
label_desc: Use image node_id 2 for calling the FCEHead obj's underlying member method code with example usage: obj.forward(feats, targets) and returns: outs