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Context
objects evaluate invocations of computations.
Invocations of TensorFlow Federated computations need to be treated
differently depending on the Context
in which they are invoked. For example:
- During top-level Python simulations, computation invocations result in the computation being serialized and evaluated by the TensorFlow native runtime.
- In
tf_computation
-annotated functions, computation invocations must import the body of the invoked function into the current TensorFlow graph.
Code can customize the way in which each of these calls are evaluated by
setting a specific Context
using a global or thread-local context stack.
Methods
invoke
@abc.abstractmethod
invoke( comp, arg )
Invokes computation comp
with argument arg
.
Args | |
---|---|
comp
|
The computation being invoked. The Python type of comp expected
here (e.g., pb.Computation . ConcreteComputation , or other) may
depend on the context. It is the responsibility of the concrete
implementation of this interface to verify that the type of comp
matches what the context is expecting.
|
arg
|
The argument passed to the computation. If no argument is passed,
this will be None . Structural argument types will be normalized into
structure.Struct s.
|
Returns | |
---|---|
The result of invocation, which is context-dependent. |