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 __init__(self, in_channels, fourier_degree=5):
      super().__init__()
      assert isinstance(in_channels, int)
  
      self.downsample_ratio = 1.0
      self.in_channels = in_channels
      self.fourier_degree = fourier_degree
      self.out_channels_cls = 4
      self.out_channels_reg = (2 * self.fourier_degree + 1) * 2
  
      self.out_conv_cls = nn.Conv2D(
          in_channels=self.in_channels,
          out_channels=self.out_channels_cls,
          kernel_size=3,
          stride=1,
          padding=1,
          groups=1,
          weight_attr=ParamAttr(
              name="cls_weights", initializer=Normal(mean=0.0, std=0.01)
          ),
          bias_attr=True,
      )
      self.out_conv_reg = nn.Conv2D(
          in_channels=self.in_channels,
          out_channels=self.out_channels_reg,
          kernel_size=3,
          stride=1,
          padding=1,
          groups=1,
          weight_attr=ParamAttr(
              name="reg_weights", initializer=Normal(mean=0.0, std=0.01)
          ),
          bias_attr=True,
      )
  
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 1 to create a new FCEHead object from inherited base classes: nn with example: obj = FCEHead(in_channels, fourier_degree)