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.
      negative_ratio (float or int): Maximum ratio of negative
          samples to positive ones in OHEM. Defaults to 3.
      resample_step (float): The step size for resampling the text center
          line (TCL). It's better not to exceed half of the minimum width.
      center_region_shrink_ratio (float): The shrink ratio of text center
          region.
      level_size_divisors (tuple(int)): The downsample ratio on each level.
      level_proportion_range (tuple(tuple(int))): The range of text sizes
          assigned to each level.
      loss_tr (dict) : The loss config used to calculate the text region
          loss. Defaults to dict(type='MaskedBalancedBCELoss').
      loss_tcl (dict) : The loss config used to calculate the text center
          line loss. Defaults to dict(type='MaskedBCELoss').
      loss_reg_x (dict) : The loss config used to calculate the regression
          loss on x axis. Defaults to dict(type='MaskedSmoothL1Loss').
      loss_reg_y (dict) : The loss config used to calculate the regression
          loss on y axis. Defaults to dict(type='MaskedSmoothL1Loss').
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docstr: Generate loss target for fcenet from a data sample.
  
  Args:
      data_sample (TextDetDataSample): The data sample.
  
  Returns:
      tuple[Tensor]: A tuple of three tensors from three different
      feature level as the targets of one prediction.
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code: def _get_target_single(self, data_sample: TextDetDataSample) -> Tuple:
      """Generate loss target for fcenet from a data sample.
  
      Args:
          data_sample (TextDetDataSample): The data sample.
  
      Returns:
          tuple[Tensor]: A tuple of three tensors from three different
          feature level as the targets of one prediction.
      """
      img_size = data_sample.img_shape[:2]
      text_polys = data_sample.gt_instances.polygons
      ignore_flags = data_sample.gt_instances.ignored
  
      p3_map, p4_map, p5_map = self._generate_level_targets(
          img_size, text_polys, ignore_flags
      )
      # to tesnor
      p3_map = torch.from_numpy(p3_map).unsqueeze(0).float()
      p4_map = torch.from_numpy(p4_map).unsqueeze(0).float()
      p5_map = torch.from_numpy(p5_map).unsqueeze(0).float()
      return p3_map, p4_map, p5_map
  
imports: from typing import Dict, List, Optional, Sequence, Tuple, Union
  import cv2
  import numpy
  import torch
  from mmdet.models.utils import multi_apply
  from numpy.fft import fft
  from numpy.linalg import norm
  from mmocr.registry import MODELS
  from mmocr.structures import TextDetDataSample
  from mmocr.utils.typing_utils import ArrayLike
  from .textsnake_module_loss import TextSnakeModuleLoss
label_desc: Use image node_id 5 for calling the FCEModuleLoss obj's underlying member method code with example usage: obj._get_target_single(data_sample) and returns: p3_map,  p4_map,  p5_map