Package tensorflow

Class Struct.StructuredValue.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<Struct.StructuredValue.Builder>
com.google.protobuf.GeneratedMessageV3.Builder<Struct.StructuredValue.Builder>
tensorflow.Struct.StructuredValue.Builder
All Implemented Interfaces:
com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable, Struct.StructuredValueOrBuilder
Enclosing class:
Struct.StructuredValue
public static final class Struct.StructuredValue.Builder extends com.google.protobuf.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