class_docstr: The class for implementing FCENet loss
  FCENet(CVPR2021): Fourier Contour Embedding for Arbitrary-shaped
      Text Detection
  
  [https://arxiv.org/abs/2104.10442]
  
  Args:
      fourier_degree (int) : The maximum Fourier transform degree k.
      num_sample (int) : The sampling points number of regression
          loss. If it is too small, fcenet tends to be overfitting.
      ohem_ratio (float): the negative/positive ratio in OHEM.
class_docstr_tok: ["The","class","for","implementing","FCENet","loss","FCENet","(","CVPR2021",")",":","Fourier","Contour","Embedding","for","Arbitrary-shaped","Text","Detection","[","https",":","\/\/arxiv.org\/abs\/2104.10442","]","Args",":","fourier_degree","(","int",")",":","The","maximum","Fourier","transform","degree","k.","num_sample","(","int",")",":","The","sampling","points","number","of","regression","loss",".","If","it","is","too","small",",","fcenet","tends","to","be","overfitting",".","ohem_ratio","(","float",")",":","the","negative\/positive","ratio","in","OHEM","."]
code: def __init__(self, fourier_degree, num_sample, ohem_ratio=3.0):
      super().__init__()
      self.fourier_degree = fourier_degree
      self.num_sample = num_sample
      self.ohem_ratio = ohem_ratio
  
file_path: PaddleOCR/ppocr/losses/det_fce_loss.py
imports: import numpy
  from paddle import nn
  import paddle
  import paddle.nn.functional
  from functools import partial
label_desc: Use image node_id 1 to create a new FCELoss object from inherited base classes: nn with example: obj = FCELoss(fourier_degree, num_sample, ohem_ratio)