code: def forward(
      self,
      x,
      context=None,
      mask=None,
      additional_tokens=None,
      n_times_crossframe_attn_in_self=0,
  ):
      if additional_tokens is not None:
          # get the number of masked tokens at the beginning of the output sequence
          n_tokens_to_mask = additional_tokens.shape[1]
          # add additional token
          x = torch.cat([additional_tokens, x], dim=1)
      q = self.to_q(x)
      context = default(context, x)
      k = self.to_k(context)
      v = self.to_v(context)
  
      if n_times_crossframe_attn_in_self:
          # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439
          assert x.shape[0] % n_times_crossframe_attn_in_self == 0
          # n_cp = x.shape[0]//n_times_crossframe_attn_in_self
          k = repeat(
              k[::n_times_crossframe_attn_in_self],
              "b ... -> (b n) ...",
              n=n_times_crossframe_attn_in_self,
          )
          v = repeat(
              v[::n_times_crossframe_attn_in_self],
              "b ... -> (b n) ...",
              n=n_times_crossframe_attn_in_self,
          )
  
      b, _, _ = q.shape
      q, k, v = map(
          lambda t: t.unsqueeze(3)
          .reshape(b, t.shape[1], self.heads, self.dim_head)
          .permute(0, 2, 1, 3)
          .reshape(b * self.heads, t.shape[1], self.dim_head)
          .contiguous(),
          (q, k, v),
      )
  
      # actually compute the attention, what we cannot get enough of
      if version.parse(xformers.__version__) >= version.parse("0.0.21"):
          # NOTE: workaround for
          # https://github.com/facebookresearch/xformers/issues/845
          max_bs = 32768
          N = q.shape[0]
          n_batches = math.ceil(N / max_bs)
          out = list()
          for i_batch in range(n_batches):
              batch = slice(i_batch * max_bs, (i_batch + 1) * max_bs)
              out.append(
                  xformers.ops.memory_efficient_attention(
                      q[batch],
                      k[batch],
                      v[batch],
                      attn_bias=None,
                      op=self.attention_op,
                  )
              )
          out = torch.cat(out, 0)
      else:
          out = xformers.ops.memory_efficient_attention(
              q, k, v, attn_bias=None, op=self.attention_op
          )
  
      # TODO: Use this directly in the attention operation, as a bias
      if exists(mask):
          raise NotImplementedError
      out = (
          out.unsqueeze(0)
          .reshape(b, self.heads, out.shape[1], self.dim_head)
          .permute(0, 2, 1, 3)
          .reshape(b, out.shape[1], self.heads * self.dim_head)
      )
      if additional_tokens is not None:
          # remove additional token
          out = out[:, n_tokens_to_mask:]
      return self.to_out(out)
  
imports: import logging
  import math
  from inspect import isfunction
  from typing import Any, Optional
  import torch
  import torch.nn.functional
  from einops import rearrange, repeat
  from packaging import version
  from torch import nn
  from torch.utils.checkpoint import checkpoint
label_desc: Use image node_id 2 for calling the MemoryEfficientCrossAttention obj's underlying member method code with example usage: obj.forward(x, context, mask, additional_tokens, n_times_crossframe_attn_in_) and returns: self