code: def forward(self, qkv):
      bs, n_ctx, width = qkv.shape
      attn_ch = width // self.n_heads // 3
      scale = 1 / math.sqrt(math.sqrt(attn_ch))
      qkv = qkv.view(bs, n_ctx, self.n_heads, -1)
      q, k, v = th.split(qkv, attn_ch, dim=-1)
      weight = th.einsum(
          "bthc,bshc->bhts", q * scale, k * scale
      )  # More stable with f16 than dividing afterwards
      wdtype = weight.dtype
      weight = th.softmax(weight.float(), dim=-1).type(wdtype)
      return th.einsum("bhts,bshc->bthc", weight, v).reshape(
          bs, n_ctx, -1
      )
  
imports: import math
  import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the QKVMultiheadAttention obj's underlying member method code with example usage: obj.forward(qkv) and returns: th