hjtran opened a new issue, #33014:
URL: https://github.com/apache/beam/issues/33014
### What happened?
I'm not exactly sure what the general condition is for this bug, but I have
at least one pipeline where using `FlatMapTuple` with a lambda results in a
weird output type hint that includes `type`.
```
import argparse
import logging
import re
import apache_beam as beam
from apache_beam.io import ReadFromText
from apache_beam.io import WriteToText
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import SetupOptions
def main(argv=None, save_main_session=True):
from typing import Tuple
def identity(x: Tuple[str, int]) -> Tuple[str, int]:
return x
with beam.Pipeline() as p:
with beam.Pipeline() as p:
A = (p
| "Generate input" >> beam.Create([('P1', [2])])
# This line
| "Flat" >> beam.FlatMapTuple(lambda k, vs: [(k, v) for v in
vs])
| "Identity" >> beam.Map(identity))
if __name__ == '__main__':
logging.getLogger().setLevel(logging.INFO)
main()
```
This fails with a type hint violation due to the FlatMap method supposedly
returning a pcollection with element type `Tuple[type, int]`.
You can fix this by adjusting the flatmap fn to return `[(str(k), v) for v
in vs]`, which leads me to believe there's something wrong in the
`trivial_inference.py` that results in this.
Beam Playground Example:
https://play.beam.apache.org/?sdk=python&shared=mSXtb0psDEN
### Issue Priority
Priority: 2 (default / most bugs should be filed as P2)
### Issue Components
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