class_docstr: Detection Head of RTMDet-Ins.
  
  Args:
      num_prototypes (int): Number of mask prototype features extracted
          from the mask head. Defaults to 8.
      dyconv_channels (int): Channel of the dynamic conv layers.
          Defaults to 8.
      num_dyconvs (int): Number of the dynamic convolution layers.
          Defaults to 3.
      mask_loss_stride (int): Down sample stride of the masks for loss
          computation. Defaults to 4.
      loss_mask (:obj:`ConfigDict` or dict): Config dict for mask loss.
docstr: split kernel head prediction to conv weight and bias.
code: def parse_dynamic_params(self, flatten_kernels: Tensor) -> tuple:
      """split kernel head prediction to conv weight and bias."""
      n_inst = flatten_kernels.size(0)
      n_layers = len(self.weight_nums)
      params_splits = list(
          torch.split_with_sizes(
              flatten_kernels, self.weight_nums + self.bias_nums, dim=1
          )
      )
      weight_splits = params_splits[:n_layers]
      bias_splits = params_splits[n_layers:]
      for i in range(n_layers):
          if i < n_layers - 1:
              weight_splits[i] = weight_splits[i].reshape(
                  n_inst * self.dyconv_channels, -1, 1, 1
              )
              bias_splits[i] = bias_splits[i].reshape(
                  n_inst * self.dyconv_channels
              )
          else:
              weight_splits[i] = weight_splits[i].reshape(
                  n_inst, -1, 1, 1
              )
              bias_splits[i] = bias_splits[i].reshape(n_inst)
  
      return weight_splits, bias_splits
  
imports: import copy
  import math
  from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, is_norm
  from mmcv.ops import batched_nms
  from mmengine.model import BaseModule, bias_init_with_prob, constant_init, normal_init
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.models.layers.transformer import inverse_sigmoid
  from mmdet.models.utils import filter_scores_and_topk, multi_apply, select_single_mlvl, sigmoid_geometric_mean
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import cat_boxes, distance2bbox, get_box_tensor, get_box_wh, scale_boxes
  from mmdet.utils import ConfigType, InstanceList, OptInstanceList, reduce_mean
  from .rtmdet_head import RTMDetHead
label_desc: Use image node_id 7 for calling the RTMDetInsHead obj's underlying member method code with example usage: obj.parse_dynamic_params(flatten_kernels) and returns: weight_splits,  bias_splits