https://github.com/GPflow/GPflow
Tip revision: a228884dff09e15af3be6b3b7fbf8bd0254cc413 authored by st-- on 17 July 2020, 13:13:44 UTC
Merge branch 'develop' into st/quickfix_num_latent
Merge branch 'develop' into st/quickfix_num_latent
Tip revision: a228884
multipledispatch.py
from multipledispatch import Dispatcher as GeneratorDispatcher
from multipledispatch.dispatcher import variadic_signature_matches
from multipledispatch.variadic import isvariadic
__all__ = ["Dispatcher"]
class Dispatcher(GeneratorDispatcher):
"""
multipledispatch.Dispatcher uses a generator to yield the
desired function implementation, which is problematic as TensorFlow's
autograph is not able to compile code that passes through generators.
This class overwrites the problematic method in the original
Dispatcher and solely makes use of simple for-loops, which are
compilable by AutoGraph.
"""
def dispatch(self, *types):
"""
Returns matching function for `types`; if not existing returns None.
"""
if types in self.funcs:
return self.funcs[types]
return self.get_first_occurrence(*types)
def get_first_occurrence(self, *types):
"""
Returns the first occurrence of a matching function
Based on `multipledispatch.Dispatcher.dispatch_iter`, which
returns an iterator of matching functions. This method uses
the same logic to select functions, but simply returns the first
element of the iterator. If no matching functions are found,
`None` is returned.
"""
n = len(types)
for signature in self.ordering:
if len(signature) == n and all(map(issubclass, types, signature)):
result = self.funcs[signature]
return result
elif len(signature) and isvariadic(signature[-1]):
if variadic_signature_matches(types, signature):
result = self.funcs[signature]
return result
return None