code: def backward(ctx, dqkv):
      seqlen_offsets = ctx.seqlen_offsets
      if seqlen_offsets is None:
          cos, sin, cos_k, sin_k, seqlen_offsets = ctx.saved_tensors
      else:
          cos, sin, cos_k, sin_k = ctx.saved_tensors
      if cos_k is None and sin_k is None and dqkv.is_contiguous():
          # Call 1 kernel instead of 2 kernels
          # We need dqkv to be contiguous so that when we reshape to combine (3, nheads)
          # dimensions, we get the same tensor
          dqk = rearrange(dqkv[:, :, :2], "b s t h d -> b s (t h) d")
          apply_rotary(
              dqk,
              cos,
              sin,
              seqlen_offsets=seqlen_offsets,
              interleaved=ctx.interleaved,
              inplace=True,
              conjugate=True,
          )
      else:
          cos_k = cos if cos_k is None else cos_k
          sin_k = sin if sin_k is None else sin_k
          dq, dk = dqkv[:, :, 0], dqkv[:, :, 1]
          apply_rotary(
              dq,
              cos,
              sin,
              seqlen_offsets,
              interleaved=ctx.interleaved,
              inplace=True,
              conjugate=True,
          )
          apply_rotary(
              dk,
              cos_k,
              sin_k,
              seqlen_offsets,
              interleaved=ctx.interleaved,
              inplace=True,
              conjugate=True,
          )
      return dqkv, None, None, None, None, None, None
  
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  from einops import rearrange, repeat
  from flash_attn.ops.triton.rotary import apply_rotary
label_desc: Use image node_id 2 for calling the ApplyRotaryEmbQKV_ obj's underlying member method code with example usage: obj.backward(ctx, dqkv) and returns: dqkv,  None,  None,  None,  None,  None,  None