code: def sample(
      self,
      p,
      x,
      conditioning,
      unconditional_conditioning,
      steps=None,
      image_conditioning=None,
  ):
      steps = steps or p.steps
      timesteps = self.get_timesteps(p, steps)
  
      extra_params_kwargs = self.initialize(p)
      parameters = inspect.signature(self.func).parameters
  
      if "timesteps" in parameters:
          extra_params_kwargs["timesteps"] = timesteps
  
      self.last_latent = x
      self.sampler_extra_args = {
          "cond": conditioning,
          "image_cond": image_conditioning,
          "uncond": unconditional_conditioning,
          "cond_scale": p.cfg_scale,
          "s_min_uncond": self.s_min_uncond,
      }
      samples = self.launch_sampling(
          steps,
          lambda: self.func(
              self.model_wrap_cfg,
              x,
              extra_args=self.sampler_extra_args,
              disable=False,
              callback=self.callback_state,
              **extra_params_kwargs,
          ),
      )
  
      if self.model_wrap_cfg.padded_cond_uncond:
          p.extra_generation_params["Pad conds"] = True
  
      return samples
  
imports: import torch
  import inspect
  import sys
  from modules import devices, sd_samplers_common, sd_samplers_timesteps_impl
  from modules.sd_samplers_cfg_denoiser import CFGDenoiser
  from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
  from modules.shared import opts
  import modules.shared
label_desc: Use image node_id 4 for calling the CompVisSampler obj's underlying member method code with example usage: obj.sample(p, x, conditioning, unconditional_conditioning, steps, image_conditioning) and returns: samples