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,
      )
      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,
      )
      return x_prev, pred_x0
  
file_path: NeMo/nemo/collections/multimodal/models/text_to_image/stable_diffusion/samplers/ddim.py
imports: import numpy
  import torch
  from tqdm import tqdm
  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
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.util import extract_into_tensor
  from nemo.collections.multimodal.parts.utils import randn_like
label_desc: Use image node_id 2 for calling the DDIMSampler 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