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: Constructs an instance of the DatasetSource from a dictionary representation.
  
  Args:
      source_dict: A dictionary representation of the DatasetSource.
  
  Returns:
      A DatasetSource instance.
docstr_tok: ["Constructs","an","instance","of","the","DatasetSource","from","a","dictionary","representation",".","Args",":","source_dict",":","A","dictionary","representation","of","the","DatasetSource",".","Returns",":","A","DatasetSource","instance","."]
code: def _from_dict(cls, source_dict: Dict[Any, Any]) -> "DatasetSource":
      """Constructs an instance of the DatasetSource from a dictionary representation.
  
      Args:
          source_dict: A dictionary representation of the DatasetSource.
  
      Returns:
          A DatasetSource instance.
  
      """
  
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 7 for calling the DatasetSource obj's underlying member method code with example usage: obj._from_dict(cls, source_dict) without return types