code: def p_mean_variance(
      self,
      unet,
      x,
      t,
      t_next,
      text_embeds,
      text_mask,
      x_low_res=None,
      cond_scale=1.0,
      thresholding_method="dynamic",
  ):
      if self.unet_type == "base":
          pred = unet.forward_with_cond_scale(
              x=x,
              time=t,
              text_embed=text_embeds,
              text_mask=text_mask,
              cond_scale=cond_scale,
          )
      elif self.unet_type == "sr":
          pred = unet.forward_with_cond_scale(
              x=x,
              x_low_res=x_low_res,
              time=t,
              text_embed=text_embeds,
              text_mask=text_mask,
              cond_scale=cond_scale,
          )
  
      if self.pred_objective == "noise":
          x_start = self.noise_scheduler.predict_start_from_noise(
              x, t=t, noise=pred
          )
      elif self.pred_objective == "x_start":
          x_start = pred
      elif self.pred_objective == "v":
          x_start = self.noise_scheduler.predict_start_from_v(
              x, t=t, v=pred
          )
      else:
          raise ValueError(f"unknown objective {self.pred_objective}")
  
      x_start = thresholding_x0(x_start, method=thresholding_method)
      mean_and_variance = self.noise_scheduler.q_posterior(
          x_start=x_start, x_t=x, t=t, t_next=t_next
      )
      return mean_and_variance, 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 2 for calling the DDPMSampler obj's underlying member method code with example usage: obj.p_mean_variance(unet, x, t, t_next, text_embeds, text_mask, x_low_res, cond_scale, thresholding_method) and returns: mean_and_variance,  x_start