code: def preprocess_uae(encoder_model):
      """
      Preprocess function with Untrained Autoencoder.
      """
      if isinstance(encoder_model, tf.keras.Model):
          preprocess_fn = partial(
              preprocess_drift_tf, model=encoder_model
          )
      else:
          device = torch.device(
              "cuda" if torch.cuda.is_available() else "cpu"
          )
          preprocess_fn = partial(
              preprocess_drift_pt, model=encoder_model, device=device
          )
      return preprocess_fn
  
imports: from functools import partial
  from importlib import import_module
  import numpy
  import tensorflow
  import torch
  import torch.nn
  from sklearn.ensemble import RandomForestClassifier
  from xgboost import XGBClassifier
  from requests.exceptions import HTTPError
  import pytest
  from pytest_cases import fixture, parametrize
  from transformers import AutoTokenizer
  from alibi_detect.cd.pytorch import UAE
  from alibi_detect.cd.pytorch import preprocess_drift
  from alibi_detect.cd.tensorflow import UAE
  from alibi_detect.cd.tensorflow import preprocess_drift
  from alibi_detect.utils.pytorch.kernels import GaussianRBF
  from alibi_detect.utils.pytorch.kernels import DeepKernel
  from alibi_detect.utils.tensorflow.kernels import GaussianRBF
  from alibi_detect.utils.tensorflow.kernels import DeepKernel
  from alibi_detect.models.pytorch import TransformerEmbedding
  from alibi_detect.models.tensorflow import TransformerEmbedding
  from alibi_detect.cd.pytorch import HiddenOutput
  from alibi_detect.cd.tensorflow import HiddenOutput
  from alibi_detect.utils.frameworks import has_keops
label_desc: Use image node_id 3 for calling a global function with example usage: preprocess_uae(encoder_model) and returns: preprocess_fn