code: def load_model_from_checkpoint_path(
      path: str, tags: Optional[List[str]] = None
  ) -> AutoTrackable:
      """
      Loads a checkpoint written by a TFKerasTrial.
  
      You should have already downloaded the checkpoint files, likely with
      :meth:`Checkpoint.download() <determined.experimental.client.Checkpoint.download()>`.
  
      The return type is a TensorFlow AutoTrackable object.
  
      Arguments:
          path (string): Top level directory to load the checkpoint from.
          tags (list string, optional): Specifies which tags are loaded from
              the TensorFlow SavedModel. See documentation for `tf.compat.v1.saved_model.load_v2
              <https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/saved_model/load_v2>`_.
      """
  
      ckpt_dir = pathlib.Path(path)
      load_data_path = ckpt_dir.joinpath("load_data.json")
      metadata_path = ckpt_dir.joinpath("metadata.json")
      if load_data_path.exists():
          with load_data_path.open() as f:
              load_data = json.load(f)
          if load_data["trial_type"] != "TFKerasTrial":
              logger.warning(
                  "Checkpoint does not appear to be a valid TFKerasTrial checkpoint, "
                  "continuing anyway..."
              )
          experiment_config = load_data["experiment_config"]
          hparams = load_data["hparams"]
          trial_cls_spec = load_data["trial_cls_spec"]
          filename = "determined-keras-model-weights"
      elif metadata_path.exists():
          # Old checkpoints (<=0.17.7) used to depend on metadata coming from the master in
          # Checkpoint.download().
          with metadata_path.open() as f:
              metadata = json.load(f)
          framework = metadata.get("framework", "")
          is_tf = (
              framework.startswith("tensorflow")
              or "tensorflow_version" in metadata
          )
          save_format = metadata.get("format")
          is_keras = save_format not in ("saved_weights", "h5")
          if not is_tf or not is_keras:
              logger.warning(
                  "Checkpoint does not appear to be a valid TFKerasTrial checkpoint, "
                  "continuing anyway..."
              )
          if save_format == "saved_weights":
              filename = "determined-keras-model-weights"
          elif save_format == "h5":
              # This is how tf.keras models were saved prior to Determined 0.13.8.
              filename = "determined-keras-model.h5"
          else:
              raise AssertionError(
                  "Unknown checkpoint format at {}".format(
                      str(ckpt_dir)
                  )
              )
          experiment_config = metadata["experiment_config"]
          hparams = metadata["hparams"]
          # When this format was in use, all entrypoints were trial classes.
          trial_cls_spec = experiment_config["entrypoint"]
      else:
          raise AssertionError(
              "Checkpoint does not have either load_data.json or metadata.json.  Checkpoints written "
              "by Determined 0.17.7 and earlier did not save enough information to be loaded "
              "directly from the files in the checkpoint.  Instead, a metadata.json was written "
              "during the call to Checkpoint.download().  If you are reading an old checkpoint "
              "directly from checkpoint storage, you can either use Checkpoint.download() instead or "
              "you can use Checkpoint.write_metatdata_file('metadata.json') to create a suitable "
              "metadata file for loading a legacy checkpoint."
          )
  
      trial_cls, trial_context = det._load_trial_for_checkpoint_export(
          ckpt_dir.joinpath("code"),
          managed_training=False,
          trial_cls_spec=trial_cls_spec,
          config=experiment_config,
          hparams=hparams,
      )
  
      trial = cast(keras.TFKerasTrial, trial_cls(trial_context))
      model = trial.build_model()
      model.load_weights(str(ckpt_dir.joinpath(filename)))
      return model
  
file_path: determined/harness/determined/keras/_load.py
imports: import json
  import logging
  import pathlib
  from typing import List, Optional, cast
  from tensorflow.python.training.tracking.tracking import AutoTrackable
  import determined
  from determined import keras
label_desc: Use image node_id 1 for calling a global function with example usage: load_model_from_checkpoint_path(path, tags) and returns: model