code: def p_sampling_fn(
      self,
      x,
      c,
      t,
      index,
      repeat_noise=False,
      use_original_steps=False,
      quantize_denoised=False,
      temperature=1.0,
      noise_dropout=0.0,
      score_corrector=None,
      corrector_kwargs=None,
      unconditional_guidance_scale=1.0,
      unconditional_conditioning=None,
      old_eps=None,
      t_next=None,
  ):
      b, *_, device = *x.shape, x.device
      e_t, model_output = self._get_model_output(
          x,
          t,
          unconditional_conditioning,
          unconditional_guidance_scale,
          score_corrector,
          c,
          corrector_kwargs,
      )
      if len(old_eps) == 0:
          # Pseudo Improved Euler (2nd order)
          x_prev, pred_x0 = self._get_x_prev_and_pred_x0(
              use_original_steps,
              b,
              index,
              device,
              x,
              t,
              model_output,
              e_t,
              quantize_denoised,
              repeat_noise,
              temperature,
              noise_dropout,
          )
          e_t_next, model_output = self._get_model_output(
              x_prev,
              t_next,
              unconditional_conditioning,
              unconditional_guidance_scale,
              score_corrector,
              c,
              corrector_kwargs,
          )
          e_t_prime = (e_t + e_t_next) / 2
      elif len(old_eps) == 1:
          # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
          e_t_prime = (3 * e_t - old_eps[-1]) / 2
      elif len(old_eps) == 2:
          # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
          e_t_prime = (
              23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]
          ) / 12
      elif len(old_eps) >= 3:
          # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
          e_t_prime = (
              55 * e_t
              - 59 * old_eps[-1]
              + 37 * old_eps[-2]
              - 9 * old_eps[-3]
          ) / 24
  
      x_prev, pred_x0 = self._get_x_prev_and_pred_x0(
          use_original_steps,
          b,
          index,
          device,
          x,
          t,
          model_output,
          e_t_prime,
          quantize_denoised,
          repeat_noise,
          temperature,
          noise_dropout,
      )
  
      return x_prev, pred_x0, e_t
  
imports: import torch
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers import Sampler
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.base_sampler import AbstractBaseSampler
label_desc: Use image node_id 3 for calling the PLMSSampler obj's underlying member method code with example usage: obj.p_sampling_fn(x, c, t, index, repeat_noise, use_original_steps, quantize_denoised, temperature, noise_dropout, score_corrector, corrector_kwargs, unconditional_guidance_scale, unconditional_conditioning, old_eps, t_next) and returns: x_prev,  pred_x0,  e_t