code: def p_sample(
      self,
      unet,
      x,
      t,
      t_next,
      text_embeds,
      text_mask,
      x_low_res=None,
      cond_scale=1.0,
      thresholding_method="dynamic",
  ):
      (
          model_mean,
          _,
          model_log_variance,
      ), x_start = self.p_mean_variance(
          unet=unet,
          x=x,
          t=t,
          t_next=t_next,
          text_embeds=text_embeds,
          text_mask=text_mask,
          cond_scale=cond_scale,
          x_low_res=x_low_res,
          thresholding_method=thresholding_method,
      )
      noise = torch.randn_like(x)
      # no noise when t == 0
      b = x.shape[0]
      is_last_sampling_timestep = (
          (t_next == 0)
          if isinstance(
              self.noise_scheduler, GaussianDiffusionContinuousTimes
          )
          else (t == 0)
      )
      nonzero_mask = (1 - is_last_sampling_timestep.type_as(x)).reshape(
          b, *((1,) * (len(x.shape) - 1))
      )
      pred = (
          model_mean
          + nonzero_mask * (0.5 * model_log_variance).exp() * noise
      )
      return pred, x_start
  
imports: import numpy
  import torch
  from einops import rearrange
  from tqdm import tqdm
  from nemo.collections.multimodal.modules.imagen.sampler.batch_ops import batch_div, batch_mul
  from nemo.collections.multimodal.modules.imagen.sampler.continuous_ddpm import GaussianDiffusionContinuousTimes
label_desc: Use image node_id 3 for calling the DDPMSampler obj's underlying member method code with example usage: obj.p_sample(unet, x, t, t_next, text_embeds, text_mask, x_low_res, cond_scale, thresholding_method) and returns: pred,  x_start