class_docstr: The class for implementing DRRG loss. This is partially adapted from
  https://github.com/GXYM/DRRG licensed under the MIT license.
  
  DRRG: `Deep Relational Reasoning Graph Network for Arbitrary Shape Text
  Detection <https://arxiv.org/abs/1908.05900>`_.
  
  Args:
      ohem_ratio (float): The negative/positive ratio in ohem. Defaults to
          3.0.
      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. Defaults to 2.0.
      resample_step (float): The step size for resampling the text center
          line. Defaults to 8.0.
      num_min_comps (int): The minimum number of text components, which
          should be larger than k_hop1 mentioned in paper. Defaults to 9.
      num_max_comps (int): The maximum number of text components. Defaults
          to 600.
      min_width (float): The minimum width of text components. Defaults to
          8.0.
      max_width (float): The maximum width of text components. Defaults to
          24.0.
      center_region_shrink_ratio (float): The shrink ratio of text center
          regions. Defaults to 0.3.
      comp_shrink_ratio (float): The shrink ratio of text components.
          Defaults to 1.0.
      comp_w_h_ratio (float): The width to height ratio of text components.
          Defaults to 0.3.
      min_rand_half_height(float): The minimum half-height of random text
          components. Defaults to 8.0.
      max_rand_half_height (float): The maximum half-height of random
          text components. Defaults to 24.0.
      jitter_level (float): The jitter level of text component geometric
          features. Defaults to 0.2.
      loss_text (dict): The loss config used to calculate the text loss.
          Defaults to ``dict(type='MaskedBalancedBCEWithLogitsLoss',
          fallback_negative_num=100, eps=1e-5)``.
      loss_center (dict): The loss config used to calculate the center loss.
          Defaults to ``dict(type='MaskedBCEWithLogitsLoss')``.
      loss_top (dict): The loss config used to calculate the top loss, which
          is a part of the height loss. Defaults to
          ``dict(type='SmoothL1Loss', reduction='none')``.
      loss_btm (dict): The loss config used to calculate the bottom loss,
          which is a part of the height loss. Defaults to
          ``dict(type='SmoothL1Loss', reduction='none')``.
      loss_sin (dict): The loss config used to calculate the sin loss.
          Defaults to ``dict(type='MaskedSmoothL1Loss')``.
      loss_cos (dict): The loss config used to calculate the cos loss.
          Defaults to ``dict(type='MaskedSmoothL1Loss')``.
      loss_gcn (dict): The loss config used to calculate the GCN loss.
          Defaults to ``dict(type='CrossEntropyLoss')``.
docstr: Jitter text components attributes.
  
  Args:
      comp_attribs (ndarray): The text component attributes.
      jitter_level (float): The jitter level of text components
          attributes.
  
  Returns:
      ndarray: The jittered text component
      attributes (x, y, h, w, cos, sin, comp_label).
docstr_tok: ["Jitter","text","components","attributes",".","Args",":","comp_attribs","(","ndarray",")",":","The","text","component","attributes",".","jitter_level","(","float",")",":","The","jitter","level","of","text","components","attributes",".","Returns",":","ndarray",":","The","jittered","text","component","attributes","(","x",",","y",",","h",",","w",",","cos",",","sin",",","comp_label",")","."]
code: def _jitter_comp_attribs(
      self, comp_attribs: ndarray, jitter_level: float
  ) -> ndarray:
      """Jitter text components attributes.
  
      Args:
          comp_attribs (ndarray): The text component attributes.
          jitter_level (float): The jitter level of text components
              attributes.
  
      Returns:
          ndarray: The jittered text component
          attributes (x, y, h, w, cos, sin, comp_label).
      """
  
      assert comp_attribs.shape[1] == 7
      assert comp_attribs.shape[0] > 0
      assert isinstance(jitter_level, float)
  
      x = comp_attribs[:, 0].reshape((-1, 1))
      y = comp_attribs[:, 1].reshape((-1, 1))
      h = comp_attribs[:, 2].reshape((-1, 1))
      w = comp_attribs[:, 3].reshape((-1, 1))
      cos = comp_attribs[:, 4].reshape((-1, 1))
      sin = comp_attribs[:, 5].reshape((-1, 1))
      comp_labels = comp_attribs[:, 6].reshape((-1, 1))
  
      x += (
          (np.random.random(size=(len(comp_attribs), 1)) - 0.5)
          * (h * np.abs(cos) + w * np.abs(sin))
          * jitter_level
      )
      y += (
          (np.random.random(size=(len(comp_attribs), 1)) - 0.5)
          * (h * np.abs(sin) + w * np.abs(cos))
          * jitter_level
      )
  
      h += (
          (np.random.random(size=(len(comp_attribs), 1)) - 0.5)
          * h
          * jitter_level
      )
      w += (
          (np.random.random(size=(len(comp_attribs), 1)) - 0.5)
          * w
          * jitter_level
      )
  
      cos += (
          (np.random.random(size=(len(comp_attribs), 1)) - 0.5)
          * 2
          * jitter_level
      )
      sin += (
          (np.random.random(size=(len(comp_attribs), 1)) - 0.5)
          * 2
          * jitter_level
      )
  
      scale = np.sqrt(1.0 / (cos**2 + sin**2 + 1e-8))
      cos = cos * scale
      sin = sin * scale
  
      jittered_comp_attribs = np.hstack(
          [x, y, h, w, cos, sin, comp_labels]
      )
  
      return jittered_comp_attribs
  
imports: from typing import Dict, List, Sequence, Tuple
  import cv2
  import numpy
  import torch
  from mmcv.image import 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 mmocr.utils import check_argument
  from .textsnake_module_loss import TextSnakeModuleLoss
label_desc: Use image node_id 8 for calling the DRRGModuleLoss obj's underlying member method code with example usage: obj._jitter_comp_attribs(comp_attribs, jitter_level) and returns: jittered_comp_attribs