class_docstr: Represents the source of a dataset used in MLflow Tracking, providing information such as
  cloud storage location, delta table name / version, etc.
class_docstr_tok: ["Represents","the","source","of","a","dataset","used","in","MLflow","Tracking",",","providing","information","such","as","cloud","storage","location",",","delta","table","name","\/","version",",","etc","."]
docstr: Obtains a JSON-compatible dictionary representation of the DatasetSource.
  
  Returns:
      A JSON-compatible dictionary representation of the DatasetSource.
docstr_tok: ["Obtains","a","JSON-compatible","dictionary","representation","of","the","DatasetSource",".","Returns",":","A","JSON-compatible","dictionary","representation","of","the","DatasetSource","."]
code: def _to_dict(self) -> Dict[str, Any]:
      """Obtains a JSON-compatible dictionary representation of the DatasetSource.
  
      Returns:
          A JSON-compatible dictionary representation of the DatasetSource.
  
      """
  
file_path: mlflow/mlflow/data/dataset_source.py
imports: import json
  from abc import abstractmethod
  from typing import Any, Dict
  from mlflow.utils.annotations import experimental
label_desc: Use image node_id 5 for calling the DatasetSource obj's underlying member method code with example usage: obj._to_dict() without return types