_mapping=column_mapping,

Let's break this down...We configure the evidently_report_step using parameters that you would normally pass to the Evidently Report object to configure and run an Evidently report. It consists of the following fields:

column_mapping: This is an EvidentlyColumnMapping object that is the exact equivalent of the ColumnMapping object in Evidently. It is used to describe the columns in the dataset and how they should be treated (e.g. as categorical, numerical, or text features).

metrics: This is a list of EvidentlyMetricConfig objects that are used to configure the metrics that should be used to generate the report in a declarative way. This is the same as configuring the metrics that go in the Evidently Report.

download_nltk_data: This is a boolean that is used to indicate whether the NLTK data should be downloaded. This is only needed if you are using Evidently reports that handle text data, which require the NLTK data to be downloaded ahead of time.

There are several ways you can reference the Evidently metrics when configuring EvidentlyMetricConfig items:

by class name: this is the easiest way to reference an Evidently metric. You can use the name of a metric or metric preset class as it appears in the Evidently documentation (e.g."DataQualityPreset", "DatasetDriftMetric").

by full class path: you can also use the full Python class path of the metric or metric preset class ( e.g. "evidently.metric_preset.DataQualityPreset", "evidently.metrics.DatasetDriftMetric"). This is useful if you want to use metrics or metric presets that are not included in Evidently library.

by passing in the class itself: you can also import and pass in an Evidently metric or metric preset class itself, e.g.:Copyfrom evidently.metrics import DatasetDriftMetric

...

evidently_report_step.with_options(
    parameters=dict(
        metrics=[EvidentlyMetricConfig.metric(DatasetDriftMetric)]
    ),
)