code: def apply_optimizations(option=None):
      global current_optimizer
  
      undo_optimizations()
  
      if len(optimizers) == 0:
          # a script can access the model very early, and optimizations would not be filled by then
          current_optimizer = None
          return ""
  
      ldm.modules.diffusionmodules.model.nonlinearity = silu
      ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
  
      sgm.modules.diffusionmodules.model.nonlinearity = silu
      sgm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
  
      if current_optimizer is not None:
          current_optimizer.undo()
          current_optimizer = None
  
      selection = option or shared.opts.cross_attention_optimization
      if selection == "Automatic" and len(optimizers) > 0:
          matching_optimizer = next(
              iter(
                  [
                      x
                      for x in optimizers
                      if x.cmd_opt
                      and getattr(shared.cmd_opts, x.cmd_opt, False)
                  ]
              ),
              optimizers[0],
          )
      else:
          matching_optimizer = next(
              iter([x for x in optimizers if x.title() == selection]),
              None,
          )
  
      if selection == "None":
          matching_optimizer = None
      elif (
          selection == "Automatic"
          and shared.cmd_opts.disable_opt_split_attention
      ):
          matching_optimizer = None
      elif matching_optimizer is None:
          matching_optimizer = optimizers[0]
  
      if matching_optimizer is not None:
          print(
              f"Applying attention optimization: {matching_optimizer.name}... ",
              end="",
          )
          matching_optimizer.apply()
          print("done.")
          current_optimizer = matching_optimizer
          return current_optimizer.name
      else:
          print("Disabling attention optimization")
          return ""
  
imports: import torch
  from torch.nn.functional import silu
  from types import MethodType
  from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches
  from modules.hypernetworks import hypernetwork
  from modules.shared import cmd_opts
  from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, xlmr_m18
  import ldm.modules.attention
  import ldm.modules.diffusionmodules.model
  import ldm.modules.diffusionmodules.openaimodel
  import ldm.models.diffusion.ddpm
  import ldm.models.diffusion.ddim
  import ldm.models.diffusion.plms
  import ldm.modules.encoders.modules
  import sgm.modules.attention
  import sgm.modules.diffusionmodules.model
  import sgm.modules.diffusionmodules.openaimodel
  import sgm.modules.encoders.modules
label_desc: Use image node_id 2 for calling a global function with example usage: apply_optimizations(option) and returns: str, current_optimizer, str