code: def launch_sampling(self, steps, func):
      self.model_wrap_cfg.steps = steps
      self.model_wrap_cfg.total_steps = self.config.total_steps(steps)
      state.sampling_steps = steps
      state.sampling_step = 0
  
      try:
          return func()
      except RecursionError:
          print(
              "Encountered RecursionError during sampling, returning last latent. "
              "rho >5 with a polyexponential scheduler may cause this error. "
              "You should try to use a smaller rho value instead."
          )
          return self.last_latent
      except InterruptedException:
          return self.last_latent
  
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 3 for calling the Sampler obj's underlying member method code with example usage: obj.launch_sampling(steps, func) and returns: func, self, self