code: def initialize(self, p) -> dict:
      self.p = p
      self.model_wrap_cfg.p = p
      self.model_wrap_cfg.mask = p.mask if hasattr(p, "mask") else None
      self.model_wrap_cfg.nmask = (
          p.nmask if hasattr(p, "nmask") else None
      )
      self.model_wrap_cfg.step = 0
      self.model_wrap_cfg.image_cfg_scale = getattr(
          p, "image_cfg_scale", None
      )
      self.eta = (
          p.eta
          if p.eta is not None
          else getattr(opts, self.eta_option_field, 0.0)
      )
      self.s_min_uncond = getattr(p, "s_min_uncond", 0.0)
  
      k_diffusion.sampling.torch = TorchHijack(p)
  
      extra_params_kwargs = {}
      for param_name in self.extra_params:
          if (
              hasattr(p, param_name)
              and param_name in inspect.signature(self.func).parameters
          ):
              extra_params_kwargs[param_name] = getattr(p, param_name)
  
      if "eta" in inspect.signature(self.func).parameters:
          if self.eta != self.eta_default:
              p.extra_generation_params[
                  self.eta_infotext_field
              ] = self.eta
  
          extra_params_kwargs["eta"] = self.eta
  
      if len(self.extra_params) > 0:
          s_churn = getattr(opts, "s_churn", p.s_churn)
          s_tmin = getattr(opts, "s_tmin", p.s_tmin)
          s_tmax = (
              getattr(opts, "s_tmax", p.s_tmax) or self.s_tmax
          )  # 0 = inf
          s_noise = getattr(opts, "s_noise", p.s_noise)
  
          if (
              "s_churn" in extra_params_kwargs
              and s_churn != self.s_churn
          ):
              extra_params_kwargs["s_churn"] = s_churn
              p.s_churn = s_churn
              p.extra_generation_params["Sigma churn"] = s_churn
          if "s_tmin" in extra_params_kwargs and s_tmin != self.s_tmin:
              extra_params_kwargs["s_tmin"] = s_tmin
              p.s_tmin = s_tmin
              p.extra_generation_params["Sigma tmin"] = s_tmin
          if "s_tmax" in extra_params_kwargs and s_tmax != self.s_tmax:
              extra_params_kwargs["s_tmax"] = s_tmax
              p.s_tmax = s_tmax
              p.extra_generation_params["Sigma tmax"] = s_tmax
          if (
              "s_noise" in extra_params_kwargs
              and s_noise != self.s_noise
          ):
              extra_params_kwargs["s_noise"] = s_noise
              p.s_noise = s_noise
              p.extra_generation_params["Sigma noise"] = s_noise
  
      return extra_params_kwargs
  
imports: import inspect
  from collections import namedtuple
  import numpy
  import torch
  from PIL import Image
  from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models
  from modules.shared import opts, state
  import k_diffusion.sampling
label_desc: Use image node_id 5 for calling the Sampler obj's underlying member method code with example usage: obj.initialize(p) and returns: extra_params_kwargs