TheNeuralBit commented on code in PR #17253:
URL: https://github.com/apache/beam/pull/17253#discussion_r856667025
##########
sdks/python/apache_beam/transforms/core.py:
##########
@@ -685,6 +690,53 @@ def infer_output_type(self, input_type):
self._strip_output_annotations(
trivial_inference.infer_return_type(self.process, [input_type])))
+ @property
+ def process_defined(self) -> bool:
+ return (
+ self.process.__func__ # type: ignore
+ if hasattr(self.process, '__self__') else self.process) != DoFn.process
+
+ @property
+ def process_batch_defined(self) -> bool:
+ return (
+ self.process_batch.__func__ # type: ignore
+ if hasattr(self.process_batch, '__self__')
+ else self.process_batch) != DoFn.process_batch
+
+ def get_input_batch_type(self) -> typing.Optional[TypeConstraint]:
+ if not self.process_batch_defined:
+ return None
+ input_type = list(
+
inspect.signature(self.process_batch).parameters.values())[0].annotation
+ if input_type == inspect.Signature.empty:
+ # TODO(BEAM-14340): Consider supporting an alternative (dynamic?)
approach
+ # for declaring input type
+ raise TypeError(
+ f"{self.__class__.__name__}.process_batch() does not have a type "
+ "annotation on it's first parameter. This is reqired for "
Review Comment:
thank you! I put in a superfluous apostrophe too :man_facepalming:
https://github.com/apache/beam/pull/17452
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