How do I pass batch_identifiers in GE v1

[kassnl09]

This is not included in the migration guide, so I need a bit of guidance. In our v0 implementation we are using a RuntimeBatchRequest object where we pass the dictionary 'batch_identifiers' that later appears in the result json:

        batch_request = RuntimeBatchRequest(
            datasource_name=data_source_name,
            data_connector_name="dataframe_connector",
            data_asset_name=data_asset_name,
            batch_identifiers={
                "effective_date": reference_date,
                "layer_name_type_identifier": pipeline_stage,
                "run_id": f"dq_run_at_{datetime.date.today().strftime('%Y%m%d_%H%M%S')}",
                "source_system_type_identifier": domain,
                "data_product_identifier": "Other",
                "showstopper": is_blocking,
            },
            runtime_parameters={"batch_data": df},
       )

I haven't yet found a way to pass these batch_identifiers anywhere at the v1 implementation. I'm evaluating in-memory spark dataframes as following:

        batch_definition = context.data_sources.get(data_source_name).get_asset(data_asset_name).get_batch_definition(batch_definition_name)
        suite = context.suites.get(expectation_suite_name)
        validation_definition = context.validation_definitions.add(
            gx.ValidationDefinition(data=batch_definition, suite=suite, name=validation_definition_name)
        )
        checkpoint = gx.Checkpoint(
            name=checkpoint_name,
            validation_definitions=[validation_definition],
            result_format={
                "result_format": "SUMMARY",
                "partial_unexpected_count": 50,
                "unexpected_index_column_names": primary_keys,
            },
        )

        return checkpoint.run(
            batch_parameters={"dataframe": df},
        )

Where do the batch_identifiers identifiers fit in the logic for v1 above? I went through all documentation and I haven't found the answer yet. Any help is highly appreciated. Thank you in advance!