code: def log_model(
      keras_model, signature, artifact_path, image_dims, domain
  ):
      """
      Log a KerasImageClassifierPyfunc model as an MLflow artifact for the current run.
  
      :param keras_model: Keras model to be saved.
      :param artifact_path: Run-relative artifact path this model is to be saved to.
      :param image_dims: Image dimensions the Keras model expects.
      :param domain: Labels for the classes this model can predict.
      """
  
      with TempDir() as tmp:
          data_path = tmp.path("image_model")
          os.mkdir(data_path)
          conf = {
              "image_dims": "/".join(map(str, image_dims)),
              "domain": "/".join(map(str, domain)),
          }
          with open(os.path.join(data_path, "conf.yaml"), "w") as f:
              yaml.safe_dump(conf, stream=f)
          keras_path = os.path.join(data_path, "keras_model")
          mlflow.tensorflow.save_model(
              model=keras_model, path=keras_path
          )
          conda_env = tmp.path("conda_env.yaml")
          with open(conda_env, "w") as f:
              f.write(
                  conda_env_template.format(
                      python_version=PYTHON_VERSION,
                      keras_version=keras.__version__,
                      tf_name=tf.__name__,  # can have optional -gpu suffix
                      tf_version=tf.__version__,
                      pip_version=pip.__version__,
                      pillow_version=PIL.__version__,
                  )
              )
  
          mlflow.pyfunc.log_model(
              artifact_path=artifact_path,
              signature=signature,
              loader_module=__name__,
              code_path=[__file__],
              data_path=data_path,
              conda_env=conda_env,
          )
  
imports: import base64
  import os
  from io import BytesIO
  from typing import Any, Dict, Optional
  import keras
  import numpy
  import pandas
  import PIL
  import pip
  import tensorflow
  import yaml
  from PIL import Image
  import mlflow
  from mlflow.utils import PYTHON_VERSION
  from mlflow.utils.file_utils import TempDir
label_desc: Use image node_id 2 for calling a global function with example usage: log_model(keras_model, signature, artifact_path, image_dims, domain) without return types