code: def forward(
      self,
      qkv,
      attn_mask=None,
      key_padding_mask=None,
      need_weights=False,
  ):
      qkv = self.rearrange(
          qkv,
          "b (three h d) s -> b s three h d",
          three=3,
          h=self.num_heads,
      )
      qkv, _ = self.inner_attn(
          qkv,
          key_padding_mask=key_padding_mask,
          need_weights=need_weights,
          causal=self.causal,
      )
      return self.rearrange(qkv, "b s h d -> b (h d) s")
  
imports: from abc import abstractmethod
  import math
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from .fp16_util import convert_module_to_f16, convert_module_to_f32
  from .nn import checkpoint, conv_nd, linear, avg_pool_nd, zero_module, normalization, timestep_embedding
label_desc: Use image node_id 2 for calling the QKVFlashAttention obj's underlying member method code with example usage: obj.forward(qkv, attn_mask, key_padding_mask, need_weights) and returns: self