apache-spark

Motivation

We frequently construct an MlflowException instance with error_code=INVALID_PARAMETER_VALUE:

import mlflow.exceptions from MlflowException
from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE

raise MlflowException(
    "error message",
    error_code=INVALID_PARAMETER_VALUE,
)

If we had a class method invalid_parameter_value:

I have a simple regression task (using a LightGBMRegressor) where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?

On more advanced versions of LakeFS (probably > = v1.0.0), we would like to remove the logic that tries to fill the generation field in DB when loading old dumps. It means we will no longer support loading dump that made with a version lower than v0.61.0.

Describe the bug
When the finalizer is called for CLR JvmObjectId objects, it calls the rm DotnetBackend method and this calls goes through the JvmBridge class. Because the rm call goes through [JvmBridge.CallJavaMethod](https://github.com/do