code: def scaled_dot_product_attention_forward(
      self, x, context=None, mask=None, **kwargs
  ):
      batch_size, sequence_length, inner_dim = x.shape
  
      if mask is not None:
          mask = self.prepare_attention_mask(
              mask, sequence_length, batch_size
          )
          mask = mask.view(batch_size, self.heads, -1, mask.shape[-1])
  
      h = self.heads
      q_in = self.to_q(x)
      context = default(context, x)
  
      context_k, context_v = hypernetwork.apply_hypernetworks(
          shared.loaded_hypernetworks, context
      )
      k_in = self.to_k(context_k)
      v_in = self.to_v(context_v)
  
      head_dim = inner_dim // h
      q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
      k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
      v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
  
      del q_in, k_in, v_in
  
      dtype = q.dtype
      if shared.opts.upcast_attn:
          q, k, v = q.float(), k.float(), v.float()
  
      # the output of sdp = (batch, num_heads, seq_len, head_dim)
      hidden_states = torch.nn.functional.scaled_dot_product_attention(
          q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False
      )
  
      hidden_states = hidden_states.transpose(1, 2).reshape(
          batch_size, -1, h * head_dim
      )
      hidden_states = hidden_states.to(dtype)
  
      # linear proj
      hidden_states = self.to_out[0](hidden_states)
      # dropout
      hidden_states = self.to_out[1](hidden_states)
      return hidden_states
  
imports: from __future__ import annotations
  import math
  import psutil
  import platform
  import torch
  from torch import einsum
  from ldm.util import default
  from einops import rearrange
  from modules import shared, errors, devices, sub_quadratic_attention
  from modules.hypernetworks import hypernetwork
  import ldm.modules.attention
  import ldm.modules.diffusionmodules.model
  import sgm.modules.attention
  import sgm.modules.diffusionmodules.model
label_desc: Use image node_id 18 for calling a global function with example usage: scaled_dot_product_attention_forward(x, context, mask) and returns: hidden_states