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: Generate mask logits from mask features with dynamic convs.
  
  Args:
      mask_feat (Tensor): Mask prototype features.
          Has shape (num_prototypes, H, W).
      kernels (Tensor): Kernel parameters for each instance.
          Has shape (num_instance, num_params)
      priors (Tensor): Center priors for each instance.
          Has shape (num_instance, 4).
  Returns:
      Tensor: Instance segmentation masks for each instance.
          Has shape (num_instance, H, W).
code: def _mask_predict_by_feat_single(
      self, mask_feat: Tensor, kernels: Tensor, priors: Tensor
  ) -> Tensor:
      """Generate mask logits from mask features with dynamic convs.
  
      Args:
          mask_feat (Tensor): Mask prototype features.
              Has shape (num_prototypes, H, W).
          kernels (Tensor): Kernel parameters for each instance.
              Has shape (num_instance, num_params)
          priors (Tensor): Center priors for each instance.
              Has shape (num_instance, 4).
      Returns:
          Tensor: Instance segmentation masks for each instance.
              Has shape (num_instance, H, W).
      """
      num_inst = priors.shape[0]
      h, w = mask_feat.size()[-2:]
      if num_inst < 1:
          return torch.empty(
              size=(num_inst, h, w),
              dtype=mask_feat.dtype,
              device=mask_feat.device,
          )
      if len(mask_feat.shape) < 4:
          mask_feat.unsqueeze(0)
  
      coord = self.prior_generator.single_level_grid_priors(
          (h, w), level_idx=0, device=mask_feat.device
      ).reshape(1, -1, 2)
      num_inst = priors.shape[0]
      points = priors[:, :2].reshape(-1, 1, 2)
      strides = priors[:, 2:].reshape(-1, 1, 2)
      relative_coord = (points - coord).permute(0, 2, 1) / (
          strides[..., 0].reshape(-1, 1, 1) * 8
      )
      relative_coord = relative_coord.reshape(num_inst, 2, h, w)
  
      mask_feat = torch.cat(
          [relative_coord, mask_feat.repeat(num_inst, 1, 1, 1)], dim=1
      )
      weights, biases = self.parse_dynamic_params(kernels)
  
      n_layers = len(weights)
      x = mask_feat.reshape(1, -1, h, w)
      for i, (weight, bias) in enumerate(zip(weights, biases)):
          x = F.conv2d(
              x, weight, bias=bias, stride=1, padding=0, groups=num_inst
          )
          if i < n_layers - 1:
              x = F.relu(x)
      x = x.reshape(num_inst, h, w)
      return x
  
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 8 for calling the RTMDetInsHead obj's underlying member method code with example usage: obj._mask_predict_by_feat_single(mask_feat, kernels, priors) and returns: x, torch