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What is the best practice for managing MLflow runs in conjunction with SparkTrials, and why is it recommended?
A
Use a separate MLflow run for each trial to simplify logging
B
Avoid using with mlflow.start_run() to prevent conflicts
C
Wrap the call to fmin() inside with mlflow.start_run() to ensure separate MLflow main runs
D
Use a single MLflow run for multiple fmin() calls to save resources
