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","."]
code: def __init__(
      self,
      df: "pyspark.sql.DataFrame",
      source: DatasetSource,
      targets: Optional[str] = None,
      name: Optional[str] = None,
      digest: Optional[str] = None,
  ):
      if targets is not None and targets not in df.columns:
          raise MlflowException(
              f"The specified Spark dataset does not contain the specified targets column"
              f" '{targets}'.",
              INVALID_PARAMETER_VALUE,
          )
  
      self._df = df
      self._targets = targets
      super().__init__(source=source, name=name, digest=digest)
  
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 1 to create a new SparkDataset object from inherited base classes: Dataset, PyFuncConvertibleDatasetMixin with example: obj = SparkDataset(df, source, targets, name, digest)