code: def __init__(
      self,
      head_hidden_dim,
      head_dim_dynamic,
      head_num_dynamic,
  ):
      super().__init__()
  
      self.hidden_dim = head_hidden_dim
      self.dim_dynamic = head_dim_dynamic
      self.num_dynamic = head_num_dynamic
      self.num_params = self.hidden_dim * self.dim_dynamic
      self.dynamic_layer = nn.Linear(
          self.hidden_dim, self.num_dynamic * self.num_params
      )
  
      self.norm1 = nn.LayerNorm(self.dim_dynamic)
      self.norm2 = nn.LayerNorm(self.hidden_dim)
  
      self.activation = nn.ReLU()
  
      pooler_resolution = 7
      num_output = self.hidden_dim * pooler_resolution**2
      self.out_layer = nn.Linear(num_output, self.hidden_dim)
      self.norm3 = nn.LayerNorm(self.hidden_dim)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import copy
  import paddle
  import paddle.nn
  from ppdet.core.workspace import register
  from ppdet.modeling.heads.roi_extractor import RoIAlign
  from ppdet.modeling.bbox_utils import delta2bbox
  from ..None import initializer
label_desc: Use image node_id 1 to create a new DynamicConv object from inherited base classes: nn with example: obj = DynamicConv(head_hidden_dim, head_dim_dynamic, head_num_dynamic)