code: def preprocess_step_drift(
      cd: Union[
          ChiSquareDrift,
          ClassifierDriftTF,
          KSDrift,
          MMDDriftTF,
          TabularDrift,
      ]
  ) -> Tuple[
      Optional[Callable],
      Dict,
      Optional[tf.keras.Model],
      Optional[TransformerEmbedding],
      Dict,
      Optional[Callable],
      bool,
  ]:
      # note: need to be able to dill tokenizers other than transformers
      preprocess_fn, preprocess_kwargs = None, {}
      model, embed, embed_args, tokenizer, load_emb = (
          None,
          None,
          {},
          None,
          False,
      )
      if isinstance(cd.preprocess_fn, partial):
          preprocess_fn = cd.preprocess_fn.func
          for k, v in cd.preprocess_fn.keywords.items():
              if isinstance(v, UAE):
                  if isinstance(
                      v.encoder.layers[0], TransformerEmbedding
                  ):  # text drift
                      # embedding
                      embed = v.encoder.layers[0].model
                      embed_args = dict(
                          embedding_type=v.encoder.layers[0].emb_type,
                          layers=v.encoder.layers[0].hs_emb.keywords[
                              "layers"
                          ],
                      )
                      load_emb = True
  
                      # preprocessing encoder
                      inputs = Input(
                          shape=cd.input_shape, dtype=tf.int64
                      )
                      v.encoder.call(inputs)
                      shape_enc = (
                          v.encoder.layers[0].output.shape[-1],
                      )
                      layers = [
                          InputLayer(input_shape=shape_enc)
                      ] + v.encoder.layers[1:]
                      model = tf.keras.Sequential(layers)
                      _ = model(tf.zeros((1,) + shape_enc))
                  else:
                      model = v.encoder
                  preprocess_kwargs["model"] = "UAE"
              elif isinstance(v, HiddenOutput):
                  model = v.model
                  preprocess_kwargs["model"] = "HiddenOutput"
              elif isinstance(v, tf.keras.Model):
                  model = v
                  preprocess_kwargs["model"] = "custom"
              elif hasattr(v, "__module__"):
                  if (
                      "transformers" in v.__module__
                  ):  # transformers tokenizer
                      tokenizer = v
                      preprocess_kwargs[k] = v.__module__
              else:
                  preprocess_kwargs[k] = v
      elif callable(cd.preprocess_fn):
          preprocess_fn = cd.preprocess_fn
      return (
          preprocess_fn,
          preprocess_kwargs,
          model,
          embed,
          embed_args,
          tokenizer,
          load_emb,
      )
  
imports: import logging
  import os
  from functools import partial
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import dill
  import tensorflow
  from tensorflow.keras.layers import Input, InputLayer
  from alibi_detect.ad import AdversarialAE, ModelDistillation
  from alibi_detect.cd import ChiSquareDrift, ClassifierDrift, KSDrift, MMDDrift, TabularDrift
  from alibi_detect.cd.tensorflow import UAE, HiddenOutput
  from alibi_detect.cd.tensorflow.classifier import ClassifierDriftTF
  from alibi_detect.cd.tensorflow.mmd import MMDDriftTF
  from alibi_detect.models.tensorflow import TransformerEmbedding
  from alibi_detect.od import LLR, IForest, Mahalanobis, OutlierAE, OutlierAEGMM, OutlierProphet, OutlierSeq2Seq, OutlierVAE, OutlierVAEGMM, SpectralResidual
  from alibi_detect.utils._types import Literal
  from alibi_detect.utils.tensorflow.kernels import GaussianRBF
  from alibi_detect.utils.missing_optional_dependency import MissingDependency
  from alibi_detect.utils.frameworks import Framework
label_desc: Use image node_id 7 for calling a global function with example usage: preprocess_step_drift(cd) and returns: preprocess_fn,  preprocess_kwargs,  model,  embed,  embed_args,  tokenizer,  load_emb