code: def __init__(
      self,
      in_channels=256,
      feature_channels=64,
      out_channels=None,
      roi_resolution=7,
      with_proj=True,
  ):
      super(DynamicConv, self).__init__()
  
      self.in_channels = in_channels
      self.feature_channels = feature_channels
      self.out_channels = out_channels if out_channels else in_channels
  
      self.num_params_in = self.in_channels * self.feature_channels
      self.num_params_out = self.out_channels * self.feature_channels
      self.dynamic_layer = nn.Linear(
          self.in_channels, self.num_params_in + self.num_params_out
      )
  
      self.norm_in = nn.LayerNorm(self.feature_channels)
      self.norm_out = nn.LayerNorm(self.out_channels)
  
      self.activation = nn.ReLU()
  
      self.with_proj = with_proj
      if self.with_proj:
          num_output = self.out_channels * roi_resolution**2
          self.fc_layer = nn.Linear(num_output, self.out_channels)
          self.fc_norm = nn.LayerNorm(self.out_channels)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import paddle
  from paddle import nn
  from ppdet.core.workspace import register
  from ppdet.modeling import initializer
  from .roi_extractor import RoIAlign
  from ..bbox_utils import delta2bbox_v2
  from ..cls_utils import _get_class_default_kwargs
  from ..layers import MultiHeadAttention
label_desc: Use image node_id 1 to create a new DynamicConv object from inherited base classes: nn with example: obj = DynamicConv(in_channels, feature_channels, out_channels, roi_resolution, with_proj)