swh:1:snp:93431e0de56bff942fc37a8298daad635afceed0
Tip revision: 435a7ffac5a7188a6886aba9f43ebc87a1dde1df authored by Artem Artemev on 20 May 2020, 13:59:30 UTC
Merge pull request #1481 from GPflow/develop
Merge pull request #1481 from GPflow/develop
Tip revision: 435a7ff
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