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Making context.plans hold a dataclass e.g.
```python
@dataclass
class PlanWithMetadata:
plan_function: PlanGenerator
params:
metadata:
```
prevents having to maintain two dictio…
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### Describe the bug
[Slack thread](https://flyte-org.slack.com/archives/CREL4QVAQ/p1663569726857659)
Failed to serialize dataclass because the value became empty for some reason after running ray…
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## Describe the issue
Issue originally reported in https://flyte-org.slack.com/archives/C01P3B761A6/p1647360364170899
```python
from dataclasses import dataclass
from dataclasses_json import d…
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## 🐛 Bug
### To Reproduce
Steps to reproduce the behavior (**always include the command you ran**):
1. run pyinstaller exe on win10
2. File "fairseq\dataclass\configs.py", line 1103, in …
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Following up on the conversations in #66, and #409.
One possible way to implement generic handling of bindings is to allow users to annotate function parameters with a [dataclass](https://docs.pyth…
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A minimum replicable example:
```python
from dataclasses import dataclass, field
import tyro
import random
@dataclass
class TestDataclass:
val: int = 1
def __post_init__(self):
…
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Example :
```py
@dataclass
class MyClass4:
i_1: bool
i_8: i8
i_16: i16
i_32: i32
i_64: i64
```
This is in the PR -> https://github.com/lcompilers/lpython/pull/2801
The …
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## Steps to reproduce
Consider:
```python
from dataclasses import dataclass
from typing import Generic, TypeVar
from dataclass_mapper import create_mapper
T = TypeVar("T")
class Som…
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The unique identifier is not unique at all. Something with the @dataclass statement makes it that the base class is actually intialized only once and therefore all properties created in a loop (during…
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Thanks for sharing this work. I've found oddly different ways of loading the kinetics dataset. Is there a Dataset / Dataloader you could recommend? I'm trying to use your weights for transfer learning…