code: def __init__(
      self,
      num_classes=80,
      feat_channels=256,
      dim_feedforward=2048,
      num_cls_convs=1,
      num_reg_convs=3,
      num_heads=8,
      dropout=0.0,
      pooler_resolution=7,
      scale_clamp=_DEFAULT_SCALE_CLAMP,
      bbox_weights=(2.0, 2.0, 1.0, 1.0),
      use_focal_loss=True,
      use_fed_loss=False,
      act_cfg=dict(type="ReLU", inplace=True),
      dynamic_conv=dict(dynamic_dim=64, dynamic_num=2),
  ) -> None:
      super().__init__()
      self.feat_channels = feat_channels
  
      # Dynamic
      self.self_attn = nn.MultiheadAttention(
          feat_channels, num_heads, dropout=dropout
      )
      self.inst_interact = DynamicConv(
          feat_channels=feat_channels,
          pooler_resolution=pooler_resolution,
          dynamic_dim=dynamic_conv["dynamic_dim"],
          dynamic_num=dynamic_conv["dynamic_num"],
      )
  
      self.linear1 = nn.Linear(feat_channels, dim_feedforward)
      self.dropout = nn.Dropout(dropout)
      self.linear2 = nn.Linear(dim_feedforward, feat_channels)
  
      self.norm1 = nn.LayerNorm(feat_channels)
      self.norm2 = nn.LayerNorm(feat_channels)
      self.norm3 = nn.LayerNorm(feat_channels)
      self.dropout1 = nn.Dropout(dropout)
      self.dropout2 = nn.Dropout(dropout)
      self.dropout3 = nn.Dropout(dropout)
  
      self.activation = build_activation_layer(act_cfg)
  
      # block time mlp
      self.block_time_mlp = nn.Sequential(
          nn.SiLU(), nn.Linear(feat_channels * 4, feat_channels * 2)
      )
  
      # cls.
      cls_module = list()
      for _ in range(num_cls_convs):
          cls_module.append(
              nn.Linear(feat_channels, feat_channels, False)
          )
          cls_module.append(nn.LayerNorm(feat_channels))
          cls_module.append(nn.ReLU(inplace=True))
      self.cls_module = nn.ModuleList(cls_module)
  
      # reg.
      reg_module = list()
      for _ in range(num_reg_convs):
          reg_module.append(
              nn.Linear(feat_channels, feat_channels, False)
          )
          reg_module.append(nn.LayerNorm(feat_channels))
          reg_module.append(nn.ReLU(inplace=True))
      self.reg_module = nn.ModuleList(reg_module)
  
      # pred.
      self.use_focal_loss = use_focal_loss
      self.use_fed_loss = use_fed_loss
      if self.use_focal_loss or self.use_fed_loss:
          self.class_logits = nn.Linear(feat_channels, num_classes)
      else:
          self.class_logits = nn.Linear(feat_channels, num_classes + 1)
      self.bboxes_delta = nn.Linear(feat_channels, 4)
      self.scale_clamp = scale_clamp
      self.bbox_weights = bbox_weights
  
imports: import copy
  import math
  import random
  import warnings
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import build_activation_layer
  from mmcv.ops import batched_nms
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS, TASK_UTILS
  from mmdet.structures import SampleList
  from mmdet.structures.bbox import bbox2roi, bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh, get_box_wh, scale_boxes
  from mmdet.utils import InstanceList
label_desc: Use image node_id 1 to create a new SingleDiffusionDetHead object from inherited base classes: nn with example: obj = SingleDiffusionDetHead(num_classes, feat_channels, dim_feedforward, num_cls_convs, num_reg_convs, num_heads, dropout, pooler_resolution, scale_clamp, bbox_weights, use_focal_loss, use_fed_loss, act_cfg, dynamic_conv)