code: def forward(
      ctx,
      q,
      k,
      v,
      cu_seqlens_q,
      cu_seqlens_k,
      max_seqlen_q,
      max_seqlen_k,
      dropout_p,
      softmax_scale,
      causal,
      is_causal_prefix,
      return_softmax,
  ):
      # Save rng_state because the backward pass will regenerate the dropout mask
      rng_state = torch.cuda.get_rng_state() if dropout_p > 0 else None
      if softmax_scale is None:
          softmax_scale = q.shape[-1] ** (-0.5)
      out, softmax_lse, S_dmask = _flash_attn_forward(
          q,
          k,
          v,
          torch.empty_like(q),
          cu_seqlens_q,
          cu_seqlens_k,
          max_seqlen_q,
          max_seqlen_k,
          # dropout_p, softmax_scale, causal=causal, is_causal_prefix=is_causal_prefix,
          dropout_p,
          softmax_scale,
          causal=causal,
          is_causal_prefix=False,
          return_softmax=return_softmax,
      )
      ctx.save_for_backward(
          q,
          k,
          v,
          out,
          softmax_lse,
          cu_seqlens_q,
          cu_seqlens_k,
          rng_state,
      )
      ctx.dropout_p = dropout_p
      ctx.max_seqlen_q = max_seqlen_q
      ctx.max_seqlen_k = max_seqlen_k
      ctx.softmax_scale = softmax_scale
      ctx.causal = causal
      return out if not return_softmax else (out, softmax_lse, S_dmask)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import flash_attn_cuda
label_desc: Use image node_id 1 for calling the FlashAttnFunc obj's underlying member method code with example usage: obj.forward(ctx, q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale, causal, is_causal_prefix, return_softmax) and returns: unknown