code: def flash_attn(self, q, k, v, mask=None):
      _, heads, q_len, _, k_len, is_cuda = (
          *q.shape,
          k.shape[-2],
          q.is_cuda,
      )
  
      # Recommended for multi-query single-key-value attention by Tri Dao
      # kv shape torch.Size([1, 512, 64]) -> torch.Size([1, 8, 512, 64])
  
      if k.ndim == 3:
          k = rearrange(k, "b ... -> b 1 ...").expand_as(q)
  
      if v.ndim == 3:
          v = rearrange(v, "b ... -> b 1 ...").expand_as(q)
  
      # Check if mask exists and expand to compatible shape
      # The mask is B L, so it would have to be expanded to B H N L
  
      if exists(mask):
          mask = rearrange(mask, "b j -> b 1 1 j")
          mask = mask.expand(-1, heads, q_len, -1)
  
      # Check if there is a compatible device for flash attention
  
      config = self.cuda_config if is_cuda else self.cpu_config
  
      # pytorch 2.0 flash attn: q, k, v, mask, dropout, causal, softmax_scale
  
      with torch.backends.cuda.sdp_kernel(**config._asdict()):
          out = F.scaled_dot_product_attention(
              q,
              k,
              v,
              attn_mask=mask,
              dropout_p=self.dropout if self.training else 0.0,
              is_causal=self.causal,
          )
  
      return out
  
imports: from collections import namedtuple
  from functools import wraps
  import torch
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
  from packaging import version
  from torch import einsum, nn
label_desc: Use image node_id 3 for calling the Attend obj's underlying member method code with example usage: obj.flash_attn(q, k, v, mask) and returns: out