check_types decorator doesnt validate after inplace mutation with method

[stephansmit]

I have this code, as stated in the docs:

from pandera.typing import DataFrame
from pandera import SchemaModel, check_types
class Schema(SchemaModel):
    id: int
    name: str

    class Config:
        strict = True

class SchemaOut(SchemaModel):
    age: int


@check_types
def fn_mutate_inplace(df: DataFrame[Schema]) -> DataFrame[SchemaOut]:
    out = df.assign(age=30).pipe(DataFrame[SchemaOut])
    out.drop(["age"], axis=1, inplace=True)
    return out


df = pd.DataFrame({
    "id": [1, 2, 3],
    "name": ["a", "b", "c"]
})

df2 = fn_mutate_inplace(df.pipe(DataFrame[Schema]))
df2

I expect that mypy will succeed, but that the check_types decorator will validate the output at runtime and therefore will fail. However, this code runs, and gives me back a dataframe which is not consistent with the SchemaOut.

Is this correct behaviour, and should I only pipe to the final schema at the very end of a function?

If I dont pipe the dataframe to SchemaOut, the code will fail and pandera will catch the error at runtime, but then mypy will fail. It seems that the check_types decorator is not checking anymore when the types are already piped in the method.

Python version 3.10.11

pandas==2.2.0
pandas-stubs==2.1.4.231227
pandera==0.18.0
pydantic==2.6.0
pydantic_core==2.16.1