code: def forward(self, x_start, text_embed, text_mask, x_lowres=None):
      if self.unet_type == "base":
          assert (
              x_lowres[0].item() == DUMMY_TENSOR.item()
          ), "Base model should have no low-resolution conditioning"
          x_lowres = None
      else:
          assert x_lowres[0].dim() not in [
              0,
              1,
          ], "SR model should have low-resolution conditioning"
  
      if self.channels_last:
          x_start = x_start.to(memory_format=torch.channels_last)
          if x_lowres is not None:
              x_lowres = x_lowres.to(memory_format=torch.channels_last)
  
      # Apply random dropout to text embedding
      text_embed = random_dropout(
          text_embed, drop_rate=self.text_drop_rate
      )
      # UNet Forward Pass
      low_res_cond = (
          {"x_low_res": x_lowres} if x_lowres is not None else {}
      )
      # UNet Forward Pass and compute loss
      loss = self.model.compute_loss(
          x0=x_start,
          text_embed=text_embed,
          text_mask=text_mask,
          time=None,  # Randomly Sample
          noise=None,  # Randomly Sample
          **low_res_cond,
      )
      return loss, {"train/loss": loss}
  
imports: import itertools
  from datetime import datetime
  from functools import partial
  from typing import Any
  import torch
  from omegaconf import DictConfig, open_dict
  from pytorch_lightning import Trainer
  from nemo.collections.multimodal.data.imagen.imagen_dataset import build_train_valid_datasets
  from nemo.collections.multimodal.models.text_to_image.imagen.precond import ContinousDDPMPrecond, EDMPrecond
  from nemo.collections.multimodal.modules.imagen.diffusionmodules.nets import EfficientUNetModel, UNetModel
  from nemo.collections.multimodal.modules.imagen.encoder.t5encoder import T5Encoder
  from nemo.collections.multimodal.modules.imagen.sampler.sampler import DDPMSampler, EDMSampler
  from nemo.collections.multimodal.parts.imagen.utils import random_dropout
  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 5 for calling the Imagen obj's underlying member method code with example usage: obj.forward(x_start, text_embed, text_mask, x_lowres) and returns: loss,  dict