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def transform(dataset, output=None, resources=None)

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>>> import cupy as cp
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>>> from cuvs.preprocessing.quantize import binary
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>>> from cuvs.neighbors import cagra
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>>> n_samples = 50000
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>>> n_features = 50
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>>> dataset = cp.random.standard_normal((n_samples, n_features),
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...                                   dtype=cp.float32)
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>>> transformed = binary.transform(dataset)
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>>>
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>>> # build a cagra index on the binarized data
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>>> params = cagra.IndexParams(metric="bitwise_hamming",
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...                            build_algo="iterative_cagra_search")
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>>> idx = cagra.build(params, transformed)