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 MLflow ColSpec schema of the Spark dataset.
docstr_tok: ["The","MLflow","ColSpec","schema","of","the","Spark","dataset","."]
code: def schema(self) -> Optional[Schema]:
      """
      The MLflow ColSpec schema of the Spark dataset.
      """
      try:
          return _infer_schema(self._df)
      except Exception as e:
          _logger.warning(
              "Failed to infer schema for Spark dataset. Exception: %s",
              e,
          )
          return None
  
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 8 for calling the SparkDataset obj's underlying member method code with example usage: obj.schema() and returns: _infer_schema, None