class_docstr: Implementation of `Grid Head <https://arxiv.org/abs/1811.12030>`_
  
  Args:
      grid_points (int): The number of grid points. Defaults to 9.
      num_convs (int): The number of convolution layers. Defaults to 8.
      roi_feat_size (int): RoI feature size. Default to 14.
      in_channels (int): The channel number of inputs features.
          Defaults to 256.
      conv_kernel_size (int): The kernel size of convolution layers.
          Defaults to 3.
      point_feat_channels (int): The number of channels of each point
          features. Defaults to 64.
      class_agnostic (bool): Whether use class agnostic classification.
          If so, the output channels of logits will be 1. Defaults to False.
      loss_grid (:obj:`ConfigDict` or dict): Config of grid loss.
      conv_cfg (:obj:`ConfigDict` or dict, optional) dictionary to
          construct and config conv layer.
      norm_cfg (:obj:`ConfigDict` or dict): dictionary to construct and
          config norm layer.
      init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or             dict]): Initialization config dict.
docstr: Compute point specific representation regions.
  
  See `Grid R-CNN Plus <https://arxiv.org/abs/1906.05688>`_ for details.
docstr_tok: ["Compute","point","specific","representation","regions",".","See","`","Grid","R-CNN","Plus","<","https",":","\/\/arxiv.org\/abs\/1906.05688",">","`","_","for","details","."]
code: def calc_sub_regions(self) -> List[Tuple[float]]:
      """Compute point specific representation regions.
  
      See `Grid R-CNN Plus <https://arxiv.org/abs/1906.05688>`_ for details.
      """
      # to make it consistent with the original implementation, half_size
      # is computed as 2 * quarter_size, which is smaller
      half_size = self.whole_map_size // 4 * 2
      sub_regions = []
      for i in range(self.grid_points):
          x_idx = i // self.grid_size
          y_idx = i % self.grid_size
          if x_idx == 0:
              sub_x1 = 0
          elif x_idx == self.grid_size - 1:
              sub_x1 = half_size
          else:
              ratio = x_idx / (self.grid_size - 1) - 0.25
              sub_x1 = max(int(ratio * self.whole_map_size), 0)
  
          if y_idx == 0:
              sub_y1 = 0
          elif y_idx == self.grid_size - 1:
              sub_y1 = half_size
          else:
              ratio = y_idx / (self.grid_size - 1) - 0.25
              sub_y1 = max(int(ratio * self.whole_map_size), 0)
          sub_regions.append(
              (sub_x1, sub_y1, sub_x1 + half_size, sub_y1 + half_size)
          )
      return sub_regions
  
imports: from typing import Dict, List, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule
  from mmengine.config import ConfigDict
  from mmengine.model import BaseModule
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.models.task_modules.samplers import SamplingResult
  from mmdet.registry import MODELS
  from mmdet.utils import ConfigType, InstanceList, MultiConfig, OptConfigType
label_desc: Use image node_id 3 for calling the GridHead obj's underlying member method code with example usage: obj.calc_sub_regions() and returns: sub_regions