code: def forward(
      self,
      x,
      x_ignored_,
      x_ignored_1_,
      attn_mask=None,
      key_padding_mask=None,
      need_weights=False,
  ):
      qkv = self.Wqkv(x)
      qkv = rearrange(
          qkv,
          "b s (three h d) -> b s three h d",
          three=3,
          h=self.num_heads,
      )
      context, attn_weights = self.inner_attn(
          qkv,
          key_padding_mask=key_padding_mask,
          need_weights=need_weights,
          causal=self.causal,
      )
      return (
          self.out_proj(rearrange(context, "b s h d -> b s (h d)")),
          attn_weights,
      )
  
file_path: flash-attention/flash_attn/flash_blocksparse_attention.py
imports: import math
  import hydra
  import torch
  import torch.nn
  from einops import rearrange
  from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
  from flash_attn.flash_blocksparse_attn_interface import convert_blockmask, flash_blocksparse_attn_func
label_desc: Use image node_id 2 for calling the FlashBlocksparseMHA obj's underlying member method code with example usage: obj.forward(x, x_ignored_, x_ignored_1_, attn_mask, key_padding_mask, need_weights) and returns: self,  attn_weights