class_docstr: dim:
  num_heads:
  qkv_bias:
  qk_scale:
  attn_drop:
  proj_drop:
class_docstr_tok: ["dim",":","num_heads",":","qkv_bias",":","qk_scale",":","attn_drop",":","proj_drop",":"]
code: def forward(self, x, einops_from, einops_to, **einops_dims):
      # x [b,14*14*curr_frames+1,embed_dim]
      h = self.num_heads
      # project x to q, k, v vaalues
      # q,k,v [b,14*14*curr_frames+1,embed_dim]
      q, k, v = self.qkv(x).chunk(3, dim=-1)
      # q,k,v [b*num_heads,14*14*curr_frames+1,embed_dim/num_heads]
      q, k, v = map(
          lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h),
          (q, k, v),
      )
      q = q * self.scale
  
      # splice out CLS token at index 1
      # cls_q [b*num_heads,1,embed_dim/num_heads] q_ [b*num_heads,14*14*curr_frames,embed_dim/num_heads]
      (cls_q, q_), (cls_k, k_), (cls_v, v_) = map(
          lambda t: (t[:, 0:1], t[:, 1:]), (q, k, v)
      )
  
      # let CLS token attend to key / values of all patches across time and space
      # cls_out [b*num_heads,1,embed_dim/num_heads]
      cls_out = attn(cls_q, k, v)
  
      # rearrange across time or space
      # q_  [b*num_heads*14*14,curr_frames,embed_dim/num_heads] or [b*num_heads*curr_frames,14*14,embed_dim/num_heads]
      q_, k_, v_ = map(
          lambda t: rearrange(
              t, f"{einops_from} -> {einops_to}", **einops_dims
          ),
          (q_, k_, v_),
      )
      # expand cls token keys and values across time or space and concat
      r = q_.shape[0] // cls_k.shape[0]
      # cls_k:
      # [b*num_heads*14*14,1,embed_dim/num_heads] or [b*num_heads*curr_frames,1,embed_dim/num_heads]
      cls_k, cls_v = map(
          lambda t: repeat(t, "b () d -> (b r) () d", r=r),
          (cls_k, cls_v),
      )
      # k_, v_
      # [b*num_heads*14*14,curr_frames+1,embed_dim/num_heads] or [b*num_heads*curr_frames,14*14+1,embed_dim/num_heads]
      k_ = torch.cat((cls_k, k_), dim=1)
      v_ = torch.cat((cls_v, v_), dim=1)
      # attention
      # out [b*num_heads*14*14,curr_frames,embed_dim/num_heads] or [b*num_heads*curr_frames,14*14,embed_dim/num_heads]
      out = attn(q_, k_, v_)
      # merge back time or space
      # out [b*num_heads,14*14*curr_frames,embed_dim/num_heads]
      out = rearrange(
          out, f"{einops_to} -> {einops_from}", **einops_dims
      )
  
      # concat back the cls token
      # out [b*num_heads,14*14*curr_frames+1,embed_dim/num_heads]
      out = torch.cat((cls_out, out), dim=1)
      # merge back the heads
      # out [b,14*14*curr_frames+1,embed_dim]
      out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
      # to out
      # x [b,14*14*curr_frames+1,embed_dim]
      x = self.proj(out)
      x = self.proj_drop(x)
      return x
  
imports: from collections import OrderedDict
  from functools import partial
  import torch
  from einops import rearrange, repeat
  from timm.models.layers import DropPath, trunc_normal_
  from torch import einsum, nn
  from towhee.models.layers.mlp import Mlp
  from towhee.models.layers.patch_embed2d import PatchEmbed2D
  from towhee.models.utils.init_vit_weights import init_vit_weights
  import logging
label_desc: Use image node_id 2 for calling the VarAttention obj's underlying member method code with example usage: obj.forward(x, einops_from, einops_to) and returns: x