class_docstr: MultiScaleAttention
class_docstr_tok: ["MultiScaleAttention"]
code: def forward(self, x, thw_shape):
      qkv = self.qkv(x)
      q, k, v = rearrange(
          qkv, "b n (qkv h d) -> qkv b h n d", qkv=3, h=self.num_heads
      )
  
      self.save_v(v)
      q, q_shape = self.atn_pool_q(q, thw_shape)
      k, _ = self.atn_pool_k(k, thw_shape)
      v, _ = self.atn_pool_v(v, thw_shape)
      dots = self.matmul1([q, k]) * self.scale
      attn = self.softmax(dots)
  
      self.save_attn(attn)
      attn.register_hook(self.save_attn_gradients)
  
      x = self.matmul2([attn, v])
      x = rearrange(x, "b h n d -> b n (h d)")
      x = self.proj(x)
      if self.drop_rate > 0.0:
          x = self.proj_drop(x)
      return x, q_shape
  
imports: import math
  from functools import partial
  from typing import Tuple, List
  import numpy
  import torch
  from torch import nn
  from torch.nn.init import trunc_normal_
  from einops import rearrange
  from towhee.models.layers.layers_with_relprop import GELU, Linear, Dropout, Conv2d, Conv3d, Softmax, Sigmoid, LayerNorm, Einsum, Add, Clone, MaxPool3d, IndexSelect
label_desc: Use image node_id 12 for calling the MultiScaleAttention obj's underlying member method code with example usage: obj.forward(x, thw_shape) and returns: x,  q_shape