code: def forward(self, batch):
      x, cond = batch
      if self.use_cached_latents:
          x = DiagonalGaussianDistribution(x)
          latents = x.sample().detach() * self.scale_factor
      else:
          latents = self.vae.encode(x).sample().detach()
          latents = latents * self.scale_factor
  
      noise = randn_like(latents, generator=self.rng)
      t = torch.randint(
          0,
          self.num_timesteps,
          (latents.shape[0],),
          generator=self.rng,
          device=latents.device,
      ).long()
      x_noisy = self.noise_scheduler(x_start=latents, t=t, noise=noise)
  
      # cond = self.text_encoder([t[0] for t in batch["prompts"]])
      # if self.with_prior_preservation:
      #     cond_prior = self.text_encoder([t[1] for t in batch["prompts"]])
      #     cond = torch.cat([cond, cond_prior], dim=0)
  
      model_output = self.unet(x_noisy, t, cond)
  
      if self.parameterization == "x0":
          target = latents
      elif self.parameterization == "eps":
          target = noise
      else:
          raise NotImplementedError()
  
      if self.with_prior_preservation:
          model_pred, model_pred_prior = torch.chunk(
              model_output, 2, dim=0
          )
          target, target_prior = torch.chunk(target, 2, dim=0)
          loss = torch.nn.functional.mse_loss(
              model_pred.float(), target.float(), reduction="mean"
          )
          prior_loss = torch.nn.functional.mse_loss(
              model_pred_prior.float(),
              target_prior.float(),
              reduction="mean",
          )
          loss = loss + prior_loss * self.prior_loss_weight
  
      else:
          loss = torch.nn.functional.mse_loss(
              target.float(), model_output.float(), reduction="mean"
          )
      return loss
  
imports: from functools import partial
  from typing import Any, Optional
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from torch._inductor import config
  from nemo.collections.multimodal.data.dreambooth.dreambooth_dataset import DreamBoothDataset
  from nemo.collections.multimodal.modules.stable_diffusion.distributions.distributions import DiagonalGaussianDistribution
  from nemo.collections.multimodal.parts.utils import randn_like
  from nemo.collections.nlp.data.language_modeling.megatron.data_samplers import MegatronPretrainingRandomSampler
  from nemo.collections.nlp.models.language_modeling.megatron_base_model import MegatronBaseModel
  from nemo.collections.nlp.modules.common.megatron.module import Float16Module
  from nemo.collections.nlp.parts.utils_funcs import get_last_rank
  from nemo.core.classes.common import Serialization
  from nemo.utils import logging
label_desc: Use image node_id 6 for calling the DreamBooth obj's underlying member method code with example usage: obj.forward(batch) and returns: loss