TheNeuralBit opened a new issue, #23952:
URL: https://github.com/apache/beam/issues/23952
### What would you like to happen?
For example:
```
output_batch_type = self.fn._get_output_batch_type_normalized(
File
"/tmp/tmp.PH23v4EA7w/venv/lib/python3.8/site-packages/apache_beam/transforms/core.py",
line 828, in _get_output_batch_type_normalized
self.get_output_batch_type(input_element_type))
File
"/tmp/tmp.PH23v4EA7w/venv/lib/python3.8/site-packages/apache_beam/transforms/core.py",
line 872, in get_output_batch_type
process_batch_type = self._get_element_type_from_return_annotation(
File
"/tmp/tmp.PH23v4EA7w/venv/lib/python3.8/site-packages/apache_beam/transforms/core.py",
line 844, in _get_element_type_from_return_annotation
raise TypeError(
TypeError: Expected Iterator in return type annotation for <bound method
BatchedGenerateSamples.process_batch of <__main__.BatchedGenerateSamples object
at 0x7f4bb1c88f70>>, did you mean Iterator[<class 'numpy.ndarray'>]?
```
This is pretty clear and actionable, but it could be a bit more informative.
It could explain that Beam DoFns are one-to-N where N>=0, so they must always
yield.
### Issue Priority
Priority: 2
### Issue Component
Component: sdk-py-core
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