code: def convert_text_encoder(
      text_encoder, tokenizer, submodule_name, args
  ):
      """Converts the text encoder component of Stable Diffusion"""
      text_encoder = text_encoder.to(dtype=torch.float32)
      out_path = _get_out_path(args, submodule_name)
      if os.path.exists(out_path):
          logger.info(
              f"`{submodule_name}` already exists at {out_path}, skipping conversion."
          )
          return
  
      # Create sample inputs for tracing, conversion and correctness verification
      text_encoder_sequence_length = tokenizer.model_max_length
  
      sample_text_encoder_inputs = {
          "input_ids": torch.randint(
              text_encoder.config.vocab_size,
              (1, text_encoder_sequence_length),
              # https://github.com/apple/coremltools/issues/1423
              dtype=torch.float32,
          )
      }
      sample_text_encoder_inputs_spec = {
          k: (v.shape, v.dtype)
          for k, v in sample_text_encoder_inputs.items()
      }
      logger.info(
          f"Sample inputs spec: {sample_text_encoder_inputs_spec}"
      )
  
      class TextEncoder(nn.Module):
          def __init__(self, with_hidden_states_for_layer=None):
              super().__init__()
              self.text_encoder = text_encoder
              self.with_hidden_states_for_layer = (
                  with_hidden_states_for_layer
              )
  
          def forward(self, input_ids):
              if self.with_hidden_states_for_layer is not None:
                  output = self.text_encoder(
                      input_ids, output_hidden_states=True
                  )
                  hidden_embeds = output.hidden_states[
                      self.with_hidden_states_for_layer
                  ]
                  if "text_embeds" in output:
                      return (hidden_embeds, output.text_embeds)
                  else:
                      return (hidden_embeds, output.pooler_output)
              else:
                  return self.text_encoder(input_ids, return_dict=False)
  
      # SD XL uses the hidden states after the encoder layers from both encoders,
      # and the pooled `text_embeds` output of the second encoder.
      hidden_layer = -2 if args.xl_version else None
      reference_text_encoder = TextEncoder(
          with_hidden_states_for_layer=hidden_layer
      ).eval()
  
      logger.info(f"JIT tracing {submodule_name}..")
      reference_text_encoder = torch.jit.trace(
          reference_text_encoder,
          (sample_text_encoder_inputs["input_ids"].to(torch.int32),),
      )
      logger.info("Done.")
  
      if args.xl_version:
          output_names = ["hidden_embeds", "pooled_outputs"]
      else:
          output_names = ["last_hidden_state", "pooled_outputs"]
      coreml_text_encoder, out_path = _convert_to_coreml(
          submodule_name,
          reference_text_encoder,
          sample_text_encoder_inputs,
          output_names,
          args,
      )
  
      # Set model metadata
      coreml_text_encoder.author = f"Please refer to the Model Card available at huggingface.co/{args.model_version}"
      if args.xl_version:
          coreml_text_encoder.license = "OpenRAIL++-M (https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)"
      else:
          coreml_text_encoder.license = "OpenRAIL (https://huggingface.co/spaces/CompVis/stable-diffusion-license)"
      coreml_text_encoder.version = args.model_version
      coreml_text_encoder.short_description = (
          "Stable Diffusion generates images conditioned on text and/or other images as input through the diffusion process. "
          "Please refer to https://arxiv.org/abs/2112.10752 for details."
      )
  
      # Set the input descriptions
      coreml_text_encoder.input_description[
          "input_ids"
      ] = "The token ids that represent the input text"
  
      # Set the output descriptions
      if args.xl_version:
          coreml_text_encoder.output_description[
              "hidden_embeds"
          ] = "Hidden states after the encoder layers"
      else:
          coreml_text_encoder.output_description[
              "last_hidden_state"
          ] = "The token embeddings as encoded by the Transformer model"
      coreml_text_encoder.output_description[
          "pooled_outputs"
      ] = "The version of the `last_hidden_state` output after pooling"
  
      coreml_text_encoder.save(out_path)
  
      logger.info(f"Saved text_encoder into {out_path}")
  
      # Parity check PyTorch vs CoreML
      if args.check_output_correctness:
          baseline_out = text_encoder(
              sample_text_encoder_inputs["input_ids"].to(torch.int32),
              output_hidden_states=args.xl_version,
              return_dict=True,
          )
          if args.xl_version:
              # TODO: maybe check pooled_outputs too
              baseline_out = baseline_out.hidden_states[
                  hidden_layer
              ].numpy()
          else:
              baseline_out = baseline_out.last_hidden_state.numpy()
  
          coreml_out = coreml_text_encoder.predict(
              {
                  k: v.numpy()
                  for k, v in sample_text_encoder_inputs.items()
              }
          )
          coreml_out = coreml_out[
              "hidden_embeds"
              if args.xl_version
              else "last_hidden_state"
          ]
          report_correctness(
              baseline_out,
              coreml_out,
              "text_encoder baseline PyTorch to reference CoreML",
          )
  
      del reference_text_encoder, coreml_text_encoder
      gc.collect()
  
imports: from python_coreml_stable_diffusion import unet, controlnet, chunk_mlprogram
  import argparse
  from collections import OrderedDict, defaultdict
  from copy import deepcopy
  import coremltools
  from diffusers import StableDiffusionPipeline, DiffusionPipeline, ControlNetModel
  import gc
  import logging
  import numpy
  import os
  import requests
  import shutil
  import time
  import re
  import pathlib
  import torch
  import torch.nn
  import torch.nn.functional
  from types import MethodType
  from transformers.models.clip import modeling_clip
label_desc: Use image node_id 11 for calling a global function with example usage: convert_text_encoder(text_encoder, tokenizer, submodule_name, args) and returns: self, hidden_embeds,  output, hidden_embeds,  output