class_docstr: WaveAttention
class_docstr_tok: ["WaveAttention"]
code: def forward(self, x, h, w):
      b, n, c = x.shape
      q = (
          self.q(x)
          .reshape(b, n, self.num_heads, c // self.num_heads)
          .permute(0, 2, 1, 3)
      )
  
      x = x.view(b, h, w, c).permute(0, 3, 1, 2)
      x_dwt = self.dwt(self.reduce(x))
      x_dwt = self.filter(x_dwt)
  
      x_idwt = self.i_dwt(x_dwt)
      x_idwt = x_idwt.view(
          b, -1, x_idwt.size(-2) * x_idwt.size(-1)
      ).transpose(1, 2)
  
      kv = self.kv_embed(x_dwt).reshape(b, c, -1).permute(0, 2, 1)
      kv = (
          self.kv(kv)
          .reshape(b, -1, 2, self.num_heads, c // self.num_heads)
          .permute(2, 0, 3, 1, 4)
      )
      k, v = kv[0], kv[1]
  
      attn = (q @ k.transpose(-2, -1)) * self.scale
      attn = attn.softmax(dim=-1)
      x = (attn @ v).transpose(1, 2).reshape(b, n, c)
      x = self.proj(torch.cat([x, x_idwt], dim=-1))
      return x
  
imports: import torch
  from torch import nn
  from timm.models.layers import DropPath
  import numpy
  from towhee.models.wave_vit.wave_vit_utils import DWT2D, IDWT2D
  from towhee.models.utils.init_vit_weights import init_vit_weights
  from towhee.models.layers.mlp import Mlp
label_desc: Use image node_id 2 for calling the WaveAttention obj's underlying member method code with example usage: obj.forward(x, h, w) and returns: x