code: def forward(self, x):
      B, C, H, W = x.shape
      feats_in = x.chunk(len(self.qkvs), dim=1)
      feats_out = []
      feat = feats_in[0]
      attn_bias = self.get_attention_biases(x.device)
      for head_idx, (qkv, dws) in enumerate(zip(self.qkvs, self.dws)):
          if head_idx > 0:
              feat = feat + feats_in[head_idx]
          feat = qkv(feat)
          q, k, v = feat.view(B, -1, H, W).split(
              [self.key_dim, self.key_dim, self.val_dim], dim=1
          )
          q = dws(q)
          q, k, v = q.flatten(2), k.flatten(2), v.flatten(2)
          q = q * self.scale
          attn = q.transpose(-2, -1) @ k
          attn = attn + attn_bias[head_idx]
          attn = attn.softmax(dim=-1)
          feat = v @ attn.transpose(-2, -1)
          feat = feat.view(B, self.val_dim, H, W)
          feats_out.append(feat)
      x = self.proj(torch.cat(feats_out, 1))
      return x
  
imports: import itertools
  from collections import OrderedDict
  from typing import Dict
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SqueezeExcite, SelectAdaptivePool2d, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 4 for calling the CascadedGroupAttention obj's underlying member method code with example usage: obj.forward(x) and returns: x