def METHOD_NAME(con100):
    geometries = FEATURE_COLLECTION_1
    s2 = con100.load_collection("S2")
    predictors = s2.aggregate_spatial(geometries, reducer="mean")
    ml_model = predictors.fit_class_random_forest(target=geometries)
    assert ml_model.flat_graph() == {
        "loadcollection1": {
            "process_id": "load_collection",
            "arguments": {"id": "S2", "spatial_extent": None, "temporal_extent": None},
        },
        "aggregatespatial1": {
            "process_id": "aggregate_spatial",
            "arguments": {
                "data": {"from_node": "loadcollection1"},
                "geometries": geometries,
                "reducer": {"process_graph": {
                    "mean1": {
                        "process_id": "mean",
                        "arguments": {"data": {"from_parameter": "data"}},
                        "result": True
                    }
                }}
            },
        },
        "fitclassrandomforest1": {
            "process_id": "fit_class_random_forest",
            "arguments": {
                "predictors": {"from_node": "aggregatespatial1"},
                "target": geometries,
                "num_trees": 100,
            },
            "result": True,
        }
    }