class_docstr: Represents a Spark dataset (e.g. data derived from a Spark Table / file directory or Delta
  Table) for use with MLflow Tracking.
class_docstr_tok: ["Represents","a","Spark","dataset","(","e.g",".","data","derived","from","a","Spark","Table","\/","file","directory","or","Delta","Table",")","for","use","with","MLflow","Tracking","."]
docstr: Args:
      base_dict: A string dictionary of base information about the
          dataset, including: name, digest, source, and source type.
  
  Returns:
      A string dictionary containing the following fields: name,
      digest, source, source type, schema (optional), profile
      (optional).
docstr_tok: ["Args",":","base_dict",":","A","string","dictionary","of","base","information","about","the","dataset",",","including",":","name",",","digest",",","source",",","and","source","type",".","Returns",":","A","string","dictionary","containing","the","following","fields",":","name",",","digest",",","source",",","source","type",",","schema","(","optional",")",",","profile","(","optional",")","."]
code: def _to_dict(self, base_dict: Dict[str, str]) -> Dict[str, str]:
      """
      Args:
          base_dict: A string dictionary of base information about the
              dataset, including: name, digest, source, and source type.
  
      Returns:
          A string dictionary containing the following fields: name,
          digest, source, source type, schema (optional), profile
          (optional).
      """
      return {
          **base_dict,
          "schema": json.dumps(
              {"mlflow_colspec": self.schema.to_dict()}
          )
          if self.schema
          else None,
          "profile": json.dumps(self.profile),
      }
  
imports: import json
  import logging
  from functools import cached_property
  from typing import TYPE_CHECKING, Any, Dict, Optional, Union
  from mlflow.data.dataset import Dataset
  from mlflow.data.dataset_source import DatasetSource
  from mlflow.data.delta_dataset_source import DeltaDatasetSource
  from mlflow.data.digest_utils import compute_spark_df_digest
  from mlflow.data.pyfunc_dataset_mixin import PyFuncConvertibleDatasetMixin, PyFuncInputsOutputs
  from mlflow.data.spark_dataset_source import SparkDatasetSource
  from mlflow.exceptions import MlflowException
  from mlflow.models.evaluation.base import EvaluationDataset
  from mlflow.protos.databricks_pb2 import INTERNAL_ERROR, INVALID_PARAMETER_VALUE
  from mlflow.types import Schema
  from mlflow.types.utils import _infer_schema
  from mlflow.utils.annotations import experimental
label_desc: Use image node_id 3 for calling the SparkDataset obj's underlying member method code with example usage: obj._to_dict(base_dict) and returns: dict