code: def _get_model_pred(
      self, batch, channels, noisy_model_input, timesteps, bsz
  ):
      if self.config.xl:
          elems_to_repeat = (
              bsz // 2 if self.config.prior_preservation else bsz
          )
          if not self.config.train_text_encoder:
              unet_added_conditions = {
                  "time_ids": self.add_time_ids.repeat(
                      elems_to_repeat, 1
                  ),
                  "text_embeds": self.unet_add_text_embeds.repeat(
                      elems_to_repeat, 1
                  ),
              }
              model_pred = self.unet(
                  noisy_model_input,
                  timesteps,
                  self.prompt_embeds.repeat(elems_to_repeat, 1, 1),
                  added_cond_kwargs=unet_added_conditions,
              ).sample
          else:
              unet_added_conditions = {
                  "time_ids": self.add_time_ids.repeat(
                      elems_to_repeat, 1
                  )
              }
              (
                  prompt_embeds,
                  pooled_prompt_embeds,
              ) = utils.encode_prompt_xl(
                  text_encoders=self.text_encoders,
                  tokenizers=None,
                  prompt=None,
                  text_input_ids_list=[
                      self.tokens_one,
                      self.tokens_two,
                  ],
              )
              unet_added_conditions.update(
                  {
                      "text_embeds": pooled_prompt_embeds.repeat(
                          elems_to_repeat, 1
                      )
                  }
              )
              prompt_embeds = prompt_embeds.repeat(
                  elems_to_repeat, 1, 1
              )
              model_pred = self.unet(
                  noisy_model_input,
                  timesteps,
                  prompt_embeds,
                  added_cond_kwargs=unet_added_conditions,
              ).sample
  
      else:
          if self.config.pre_compute_text_embeddings:
              encoder_hidden_states = batch["input_ids"]
          else:
              encoder_hidden_states = utils.encode_prompt(
                  self.text_encoder1,
                  batch["input_ids"],
                  batch["attention_mask"],
                  text_encoder_use_attention_mask=self.config.text_encoder_use_attention_mask,
              )
  
          if (
              self.accelerator.unwrap_model(
                  self.unet
              ).config.in_channels
              == channels * 2
          ):
              noisy_model_input = torch.cat(
                  [noisy_model_input, noisy_model_input], dim=1
              )
  
          if self.config.class_labels_conditioning == "timesteps":
              class_labels = timesteps
          else:
              class_labels = None
  
          model_pred = self.unet(
              noisy_model_input,
              timesteps,
              encoder_hidden_states,
              class_labels=class_labels,
          ).sample
  
      return model_pred
  
imports: import itertools
  import math
  import os
  import shutil
  import torch
  import torch.nn.functional
  from diffusers import StableDiffusionXLPipeline
  from diffusers.loaders import LoraLoaderMixin, text_encoder_lora_state_dict
  from diffusers.optimization import get_scheduler
  from huggingface_hub import create_repo, upload_folder
  from tqdm import tqdm
  from autotrain import logger
  from autotrain.trainers.dreambooth import utils
label_desc: Use image node_id 9 for calling the Trainer obj's underlying member method code with example usage: obj._get_model_pred(batch, channels, noisy_model_input, timesteps, bsz) and returns: model_pred