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  #
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  """Great Expectations data validation standard step."""
  
  from typing import Any, Dict, List, Optional
  
  import pandas as pd
  from great_expectations.checkpoint.types.checkpoint_result import (  # type: ignore[import-untyped]
      CheckpointResult,
  )
  
  from zenml import step
  from zenml.integrations.great_expectations.data_validators.ge_data_validator import (
      GreatExpectationsDataValidator,
  )
  
  
  @step
  def great_expectations_validator_step(
      dataset: pd.DataFrame,
      expectation_suite_name: str,
      data_asset_name: Optional[str] = None,
      action_list: Optional[List[Dict[str, Any]]] = None,
      exit_on_error: bool = False,
  ) -> CheckpointResult:
      """Shortcut function to create a new instance of the GreatExpectationsValidatorStep step.
  
      The returned GreatExpectationsValidatorStep can be used in a pipeline to
      validate an input pd.DataFrame dataset and return the result as a Great
      Expectations CheckpointResult object. The validation results are also
      persisted in the Great Expectations validation store.
  
      Args:
          dataset: The dataset to run the expectation suite on.
          expectation_suite_name: The name of the expectation suite to use to
              validate the dataset.
          data_asset_name: The name of the data asset to use to identify the
              dataset in the Great Expectations docs.
          action_list: A list of additional Great Expectations actions to run
              after the validation check.
          exit_on_error: Set this flag to raise an error and exit the pipeline
              early if the validation fails.
  
      Returns:
          The Great Expectations validation (checkpoint) result.
  
      Raises:
          RuntimeError: if the step is configured to exit on error and the
              data validation failed.
      """
      data_validator = GreatExpectationsDataValidator.get_active_data_validator()
  
      results = data_validator.data_validation(
          dataset,
          expectation_suite_name=expectation_suite_name,
          data_asset_name=data_asset_name,
          action_list=action_list,
      )
  
      if exit_on_error and not results.success:
          raise RuntimeError(
              "The Great Expectations validation failed. Check "
              "the logs or the Great Expectations data docs for more "
              "information."
          )
  
      return results
  