code: def __init__(
      self,
      feat_channels: int,
      dynamic_dim: int = 64,
      dynamic_num: int = 2,
      pooler_resolution: int = 7,
  ) -> None:
      super().__init__()
  
      self.feat_channels = feat_channels
      self.dynamic_dim = dynamic_dim
      self.dynamic_num = dynamic_num
      self.num_params = self.feat_channels * self.dynamic_dim
      self.dynamic_layer = nn.Linear(
          self.feat_channels, self.dynamic_num * self.num_params
      )
  
      self.norm1 = nn.LayerNorm(self.dynamic_dim)
      self.norm2 = nn.LayerNorm(self.feat_channels)
  
      self.activation = nn.ReLU(inplace=True)
  
      num_output = self.feat_channels * pooler_resolution**2
      self.out_layer = nn.Linear(num_output, self.feat_channels)
      self.norm3 = nn.LayerNorm(self.feat_channels)
  
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 DynamicConv object from inherited base classes: nn with example: obj = DynamicConv(feat_channels, dynamic_dim, dynamic_num, pooler_resolution)