Class Struct.StructuredValue.Builder

All Implemented Interfaces:
Message.Builder, MessageLite.Builder, MessageLiteOrBuilder, MessageOrBuilder, Cloneable, Struct.StructuredValueOrBuilder
Enclosing class:
Struct.StructuredValue

public static final class Struct.StructuredValue.Builder extends GeneratedMessageV3.Builder<Struct.StructuredValue.Builder> implements Struct.StructuredValueOrBuilder
`StructuredValue` represents a dynamically typed value representing various
data structures that are inspired by Python data structures typically used in
TensorFlow functions as inputs and outputs.
For example when saving a Layer there may be a `training` argument. If the
user passes a boolean True/False, that switches between two concrete
TensorFlow functions. In order to switch between them in the same way after
loading the SavedModel, we need to represent "True" and "False".
A more advanced example might be a function which takes a list of
dictionaries mapping from strings to Tensors. In order to map from
user-specified arguments `[{"a": tf.constant(1.)}, {"q": tf.constant(3.)}]`
after load to the right saved TensorFlow function, we need to represent the
nested structure and the strings, recording that we have a trace for anything
matching `[{"a": tf.TensorSpec(None, tf.float32)}, {"q": tf.TensorSpec([],
tf.float64)}]` as an example.
Likewise functions may return nested structures of Tensors, for example
returning a dictionary mapping from strings to Tensors. In order for the
loaded function to return the same structure we need to serialize it.
This is an ergonomic aid for working with loaded SavedModels, not a promise
to serialize all possible function signatures. For example we do not expect
to pickle generic Python objects, and ideally we'd stay language-agnostic.
Protobuf type tensorflow.StructuredValue