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: The Spark DataFrame instance.
  
  Returns:
      The Spark DataFrame instance.
docstr_tok: ["The","Spark","DataFrame","instance",".","Returns",":","The","Spark","DataFrame","instance","."]
code: def df(self):
      """The Spark DataFrame instance.
  
      Returns:
          The Spark DataFrame instance.
  
      """
      return self._df
  
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 4 for calling the SparkDataset obj's underlying member method code with example usage: obj.df() and returns: self