class_docstr: Modified multi-head Attention.
  
  Args:
      dim (`int`):
          Dimension of features.
      num_heads (`int`):
          Number of heads.
      qkv_bias (`bool`):
          Flag to control if add bias to qkv layer.
      qk_scale (`float`):
          Number to scale qk.
      attn_drop (`float`):
          Drop rate of attention layer.
      proj_drop (`float`):
          Drop rate of projection layer.
class_docstr_tok: ["Modified","multi-head","Attention",".","Args",":","dim","(","`","int","`",")",":","Dimension","of","features",".","num_heads","(","`","int","`",")",":","Number","of","heads",".","qkv_bias","(","`","bool","`",")",":","Flag","to","control","if","add","bias","to","qkv","layer",".","qk_scale","(","`","float","`",")",":","Number","to","scale","qk",".","attn_drop","(","`","float","`",")",":","Drop","rate","of","attention","layer",".","proj_drop","(","`","float","`",")",":","Drop","rate","of","projection","layer","."]
code: def forward(
      self, x, mask, whether, einops_from, einops_to, **einops_dims
  ):
      h = self.num_heads
      # project x to q, k, v values
      q, k, v = self.qkv(x).chunk(3, dim=-1)
      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
      mask = repeat(mask, "b d -> (b r) d", r=self.num_heads)
      n_f = int(einops_dims["f"])
      # splice out CLS token at index 1
      (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
      if whether is not True:
          cls_out = attn(cls_q, k, v)
      else:
          cls_mask = mask[:, 0:1]
          mask_ = mask[:, 1:]
          mask_ = mask_.repeat(1, n_f)
          mask_tile = torch.cat((cls_mask, mask_), dim=1)
          cls_out = attn_mask(cls_q, k, v, mask_tile)
      # rearrange across time or space
      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, cls_v = map(
          lambda t: repeat(t, "b () d -> (b r) () d", r=r),
          (cls_k, cls_v),
      )
  
      k_ = torch.cat((cls_k, k_), dim=1)
      v_ = torch.cat((cls_v, v_), dim=1)
  
      # attention
      if whether is not True:
          out = attn(q_, k_, v_)
      else:
          mask_tile = mask.repeat_interleave(n_f, 0)
          out = attn_mask(q_, k_, v_, mask_tile)
  
      # merge back time or space
      out = rearrange(
          out, f"{einops_to} -> {einops_from}", **einops_dims
      )
  
      # concat back the cls token
      out = torch.cat((cls_out, out), dim=1)
  
      # merge back the heads
      out = rearrange(out, "(b h) n d -> b n (h d)", h=h)
      # to out
      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
  from towhee.models.frozen_in_time.frozen_video_transformer import attn
label_desc: Use image node_id 2 for calling the VarAttention obj's underlying member method code with example usage: obj.forward(x, mask, whether, einops_from, einops_to) and returns: x