---

name: load-and-wrap description: "Resolve TensorFlow Hub handles, load or wrap SavedModels with KerasLayer, and use the feature-column text embedding submodule safely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Load and Wrap

Use this sub-skill when the task is to inspect, resolve, or load a TensorFlow Hub handle, wrap a SavedModel with KerasLayer, or use the feature-column text embedding helper safely in current TensorFlow Hub workflows.

Do not use this sub-skill for exporting new SavedModels or text embedding modules. That workflow belongs to the sibling export-and-save route.

Quick routing

• Read references/api-reference.md for the verified public surface, import paths, and the APIs that are absent in this package version.
• Read references/load-resolve-cache.md when the issue is about local paths, smart HTTP/HTTPS handles, cache location, compressed versus uncompressed model load format, locks, descriptors, or archive validation.
• Read references/keras-layer.md when the issue is about signature, signature_outputs_as_dict, output_key, output_shape, arguments, load_options, trainability, or Keras 3 / tf_keras compatibility.
• Read references/feature-column-v2.md when the issue is about tensorflow_hub.feature_column_v2.text_embedding_column_v2, DenseFeatures, parse specs, or the top-level AttributeError recovery path.
• Read references/troubleshooting.md when the symptom is an import failure, handle/path failure, cache/download lock problem, corrupt archive, signature mismatch, trainable=True error, or feature-column import mistake.
• Run scripts/smoke_load_and_wrap.py for a local no-download sanity check. Add --feature-column when you also want to verify the feature-column path.

Quick workflow

1. Decide whether the input is a local SavedModel directory, a remote handle, or an already-loaded callable object.
2. If you need to inspect or normalize a handle, call hub.resolve(handle) first.
3. If you need the raw loaded object, call hub.load(handle) on a string handle.
4. If you need a Keras layer, wrap the handle with hub.KerasLayer(...) and choose the right output-selection mode before training or serialization.
5. If you need text embeddings inside feature columns, import tensorflow_hub.feature_column_v2 directly and build DenseFeatures around the returned column.
6. Validate the smallest possible local SavedModel with the bundled smoke script before touching any network-backed handle or large model.

Route notes

• hub.load and hub.resolve are string-handle APIs.
• KerasLayer can accept either a callable object or a string handle. Use the reference doc when you need to decide which one fits the export you have.
• A SavedModel that only exposes a signature needs explicit output selection. A callable SavedModel usually does not.
• trainable=True is not a general fix for signatures. If a model must be fine-tuned, the export has to support that path.
• The feature-column helper is only available through the submodule import path in this package version.

Keep in scope

• Loading and wrapping existing TensorFlow Hub assets.
• Diagnosing how the current package interprets handles, signatures, outputs, and cache settings.
• Safe feature-column consumption of text embedding SavedModels.

Route elsewhere

• Exporting or refreshing a SavedModel, module, or embedding archive -> export-and-save
• Broader package install or import problems -> references/troubleshooting.md
• Any workflow that depends on a public symbol not listed in references/api-reference.md