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 string representing the source type of the dataset.
  
  Returns:
      A string representing the source type of the dataset, e.g. "s3", "delta_table", ...
docstr_tok: ["Obtains","a","string","representing","the","source","type","of","the","dataset",".","Returns",":","A","string","representing","the","source","type","of","the","dataset",",","e.g",".","``","s3","''",",","``","delta_table","''",",","..."]
code: def _get_source_type() -> str:
      """Obtains a string representing the source type of the dataset.
  
      Returns:
          A string representing the source type of the dataset, e.g. "s3", "delta_table", ...
  
      """
  
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 1 for calling the DatasetSource obj's underlying member method code with example usage: obj._get_source_type() without return types