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Abstract interface for data backends.

A data backend is a component that can resolve symbolic references to data as URIs, and locally materialize the associated payloads. Data backends are used in tandem with the data_executor that queries them as it encounters the data building block.



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Materializes data with the given type_spec.

The form of the materialized payload must be such that it can be understood by the downstream components of the executor stack. For example, if the data backend is plugged into a stack based on an eager TensorFlow executor, the accepted forms of payload would include Numpy-like objects, tensors, as well as instances of eager tf.data.Dataset, and structures thereof. It is the responsibility of the code that constructs the executor stack with the given data backend to ensure that the types of payload materialized are compatible with what the downstream components of the executor stack can accept.

data A symbolic reference to the data to be materialized locally. Must be an instance of pb.Data.
type_spec An instance of computation_types.Type that represents the type of the data payload being materialized.

The materialized payload.