code: def create_t5_encoder_decoder(pretrained_version="t5-base"):
      """Generates an encoder and a decoder model with a language model head from a pretrained huggingface model
  
      Args:
          pretrained_version (str): Name of a pretrained model, or path to a pretrained / finetuned version of T5
  
      Returns:
          simplified_encoder: pytorch t5 encoder with a wrapper to output only the hidden states
          decoder_with_lm_head: pytorch t5 decoder with a language modeling head
      """
  
      # T5 is an encoder / decoder model with a language modeling head on top.
      # We need to separate those out for efficient language generation
      model = T5ForConditionalGeneration.from_pretrained(
          pretrained_version
      )
  
      return turn_model_into_encoder_decoder(model)
  
imports: from transformers import T5Tokenizer, T5ForConditionalGeneration
  from .models import CombinedDecoder, SimplifiedT5Encoder
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: create_t5_encoder_decoder(pretrained_version) and returns: turn_model_into_encoder_decoder