def proto(self):
if self._argument is not None:
call = pb.Call(
function=self._function.proto, argument=self._argument.proto)
else:
call = pb.Call(function=self._function.proto)
return pb.Computation(
type=type_serialization.serialize_type(self.type_signature), call=call)
def create_dummy_computation_call():
function = executor_test_utils.create_dummy_empty_tensorflow_computation()
value = pb.Computation(type=type_serialization.serialize_type(
computation_types.NamedTupleType([])),
call=pb.Call(function=function))
type_signature = computation_types.NamedTupleType([])
return value, type_signature
def create_dummy_computation_call():
"""Returns a call computation and type."""
fn, fn_type = create_dummy_computation_tensorflow_constant()
type_signature = fn_type.result
value = pb.Computation(
type=type_serialization.serialize_type(type_signature),
call=pb.Call(function=fn))
return value, type_signature
def create_dummy_computation_tuple():
"""Returns a tuple computation and type."""
names = ['a', 'b', 'c']
fn, fn_type = create_dummy_computation_tensorflow_constant()
element_value = pb.Computation(
type=type_serialization.serialize_type(fn_type),
call=pb.Call(function=fn))
element_type = fn_type.result
elements = [pb.Struct.Element(name=n, value=element_value) for n in names]
type_signature = computation_types.StructType(
(n, element_type) for n in names)
value = pb.Computation(
type=type_serialization.serialize_type(type_signature),
struct=pb.Struct(element=elements))
return value, type_signature
