class_docstr: Dynamic ReLU.
class_docstr_tok: ["Dynamic","ReLU","."]
code: def forward(self, x) -> Tensor:
      x_out = x
      b, c, h, w = x.size()
      x = self.avg_pool(x).view(b, c)
      x = self.fc(x).view(b, -1, 1, 1)
  
      a1, b1, a2, b2 = torch.split(x, self.out_channels, dim=1)
      a1 = (a1 - 0.5) * 2 + 1.0
      a2 = (a2 - 0.5) * 2
      b1 = b1 - 0.5
      b2 = b2 - 0.5
      out = torch.max(x_out * a1 + b1, x_out * a2 + b2)
      return out
  
imports: import copy
  import math
  from typing import Callable, List, Optional, Sequence, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import Scale
  from mmcv.ops.modulated_deform_conv import ModulatedDeformConv2d
  from mmengine.config import ConfigDict
  from mmengine.model import BaseModel
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import cat_boxes
  from mmdet.utils import InstanceList, OptInstanceList, reduce_mean
  from ..utils import BertEncoderLayer, VLFuse, filter_scores_and_topk, permute_and_flatten, select_single_mlvl, unpack_gt_instances
  from ..utils.vlfuse_helper import MAX_CLAMP_VALUE
  from .atss_head import ATSSHead
label_desc: Use image node_id 2 for calling the DyReLU obj's underlying member method code with example usage: obj.forward(x) and returns: out