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
def create_lambda_empty_struct() -> pb.Computation:
"""Returns a lambda computation returning an empty struct.
Has the type signature:
( -> <>)
Returns:
An instance of `pb.Computation`.
"""
result_type = computation_types.StructType([])
type_signature = computation_types.FunctionType(None, result_type)
result = pb.Computation(
type=type_serialization.serialize_type(result_type),
struct=pb.Struct(element=[]))
fn = pb.Lambda(parameter_name=None, result=result)
# We are unpacking the lambda argument here because `lambda` is a reserved
# keyword in Python, but it is also the name of the parameter for a
# `pb.Computation`.
# https://developers.google.com/protocol-buffers/docs/reference/python-generated#keyword-conflicts
return pb.Computation(
type=type_serialization.serialize_type(type_signature), **{'lambda': fn}) # pytype: disable=wrong-keyword-args