code: def text_proj_from_original_config():
      # From the conditional unet constructor where the dimension of the projected time embeddings is
      # constructed
      time_embed_dim = DECODER_CONFIG["block_out_channels"][0] * 4
  
      cross_attention_dim = DECODER_CONFIG["cross_attention_dim"]
  
      model = UnCLIPTextProjModel(
          time_embed_dim=time_embed_dim,
          cross_attention_dim=cross_attention_dim,
      )
  
      return model
  
imports: import argparse
  import tempfile
  import torch
  from accelerate import load_checkpoint_and_dispatch
  from transformers import CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import UnCLIPPipeline, UNet2DConditionModel, UNet2DModel
  from diffusers.models.prior_transformer import PriorTransformer
  from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
  from diffusers.schedulers.scheduling_unclip import UnCLIPScheduler
label_desc: Use image node_id 7 for calling a global function with example usage: text_proj_from_original_config() and returns: model