class_docstr: The class for implementing TextSnake loss. This is partially adapted
  from https://github.com/princewang1994/TextSnake.pytorch.
  
  TextSnake: `A Flexible Representation for Detecting Text of Arbitrary
  Shapes <https://arxiv.org/abs/1807.01544>`_.
  
  Args:
      ohem_ratio (float): The negative/positive ratio in ohem.
      downsample_ratio (float): Downsample ratio. Defaults to 1.0. TODO:
          remove it.
      orientation_thr (float): The threshold for distinguishing between
          head edge and tail edge among the horizontal and vertical edges
          of a quadrangle.
      resample_step (float): The step of resampling.
      center_region_shrink_ratio (float): The shrink ratio of text center.
      loss_text (dict): The loss config used to calculate the text loss.
      loss_center (dict): The loss config used to calculate the center loss.
      loss_radius (dict): The loss config used to calculate the radius loss.
      loss_sin (dict): The loss config used to calculate the sin loss.
      loss_cos (dict): The loss config used to calculate the cos loss.
docstr: Compute the sin of the angle between vector and x-axis.
docstr_tok: ["Compute","the","sin","of","the","angle","between","vector","and","x-axis","."]
code: def vector_sin(self, vec: ndarray) -> float:
      """Compute the sin of the angle between vector and x-axis."""
      assert len(vec) == 2
      return vec[1] / (norm(vec) + self.eps)
  
imports: from typing import Dict, List, Sequence, Tuple
  import cv2
  import numpy
  import torch
  from mmcv.image import impad, imrescale
  from mmdet.models.utils import multi_apply
  from numpy import ndarray
  from numpy.linalg import norm
  from torch import Tensor
  from mmocr.registry import MODELS
  from mmocr.structures import TextDetDataSample
  from .seg_based_module_loss import SegBasedModuleLoss
label_desc: Use image node_id 16 for calling the TextSnakeModuleLoss obj's underlying member method code with example usage: obj.vector_sin(vec) and returns: unknown