code: def forward(self, x, attn_mask=None, memories=None):
      x = self.norm(x)
  
      x_kv = x  # input for key / values projection
  
      if exists(memories):
          # add memories to key / values if it is passed in
          memories = (
              repeat(memories, "n d -> b n d", b=x.shape[0])
              if memories.ndim == 2
              else memories
          )
          x_kv = torch.cat((x_kv, memories), dim=1)
  
      qkv = (self.to_q(x), *self.to_kv(x_kv).chunk(2, dim=-1))
      q, k, v = map(
          lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads),
          qkv,
      )
  
      dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
  
      if exists(attn_mask):
          dots = dots.masked_fill(
              ~attn_mask, -torch.finfo(dots.dtype).max
          )
  
      attn = self.attend(dots)
      attn = self.dropout(attn)
  
      out = torch.matmul(attn, v)
      out = rearrange(out, "b h n d -> b n (h d)")
      return self.to_out(out)
  
imports: import torch
  from torch import nn
  import torch.nn.functional
  from einops import rearrange, repeat
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.forward(x, attn_mask, memories) and returns: self