code: def forward(self, x, q_global: Optional[torch.Tensor] = None):
      B, N, C = x.shape
      if self.use_global and q_global is not None:
          _assert(
              x.shape[-1] == q_global.shape[-1],
              "x and q_global seq lengths should be equal",
          )
  
          kv = self.qkv(x)
          kv = kv.reshape(
              B, N, 2, self.num_heads, self.head_dim
          ).permute(2, 0, 3, 1, 4)
          k, v = kv.unbind(0)
  
          q = q_global.repeat(B // q_global.shape[0], 1, 1, 1)
          q = q.reshape(B, N, self.num_heads, self.head_dim).permute(
              0, 2, 1, 3
          )
      else:
          qkv = (
              self.qkv(x)
              .reshape(B, N, 3, self.num_heads, self.head_dim)
              .permute(2, 0, 3, 1, 4)
          )
          q, k, v = qkv.unbind(0)
      q = q * self.scale
  
      attn = (
          q @ k.transpose(-2, -1).contiguous()
      )  # NOTE contiguous() fixes an odd jit bug in PyTorch 2.0
      attn = self.rel_pos(attn)
      attn = attn.softmax(dim=-1)
      attn = self.attn_drop(attn)
  
      x = (attn @ v).transpose(1, 2).reshape(B, N, C)
      x = self.proj(x)
      x = self.proj_drop(x)
      return x
  
imports: import math
  from functools import partial
  from typing import Callable, List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.utils.checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, to_ntuple, Mlp, ClassifierHead, LayerNorm2d, get_attn, get_act_layer, get_norm_layer, RelPosBias, _assert
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._manipulate import named_apply
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the WindowAttentionGlobal obj's underlying member method code with example usage: obj.forward(x, q_global) and returns: x