How to use the sdgym.utils.data.utils.verify function in sdgym

# Store Meta Files
    meta = []
    for i in range(2):
        meta.append({
                "name": str(i),
                "type": "continuous",
                "min": int(np.min(samples[:,i].astype('float'))) - 1,
                "max": int(np.max(samples[:,i].astype('float'))) + 1
        })
    # Store simulated data
    with open("{}/{}.json".format(output_dir, dist), 'w') as f:
        json.dump(meta, f, sort_keys=True, indent=4, separators=(',', ': '))
    np.savez("{}/{}.npz".format(output_dir, dist), train=samples[:len(samples)//2], test=samples[len(samples)//2:])

    utils.verify("{}/{}.npz".format(output_dir, dist),
        "{}/{}.json".format(output_dir, dist))

"i2s": [str(x) for x in range(10)]
    })

    with open("{}/{}.json".format(output_dir, name), 'w') as f:
        json.dump(meta, f, sort_keys=True, indent=4, separators=(',', ': '))


    np.random.shuffle(t_train)

    t_train = t_train.astype('int8')
    t_test = t_test.astype('int8')

    np.savez("{}/{}.npz".format(output_dir, name), train=t_train, test=t_test)


    verify("{}/{}.npz".format(output_dir, name),
            "{}/{}.json".format(output_dir, name))


    ## Sample
    for i in range(5):
        img = t_train[i][:-1].reshape([wh, wh]) * 255
        lb = t_train[i][-1]
        cv2.imwrite('{}/{}_{}_{}.png'.format(temp_dir, name, i, lb),img)

})

    tdata = df.values.astype('float32')

    np.random.seed(0)
    np.random.shuffle(tdata)

    t_train = tdata[:-8000]
    t_test = tdata[-8000:]

    name = "news"
    with open("{}/{}.json".format(output_dir, name), 'w') as f:
        json.dump(meta, f, sort_keys=True, indent=4, separators=(',', ': '))
    np.savez("{}/{}.npz".format(output_dir, name), train=t_train, test=t_test)

    verify("{}/{}.npz".format(output_dir, name),
            "{}/{}.json".format(output_dir, name))

tdata = project_table(df, meta)

    np.random.seed(0)
    np.random.shuffle(tdata)

    t_train = tdata[:-100000]
    t_test = tdata[-100000:]

    name = "intrusion"
    with open("{}/{}.json".format(output_dir, name), 'w') as f:
        json.dump(meta, f, sort_keys=True, indent=4, separators=(',', ': '))
    np.savez("{}/{}.npz".format(output_dir, name), train=t_train, test=t_test)

    verify("{}/{}.npz".format(output_dir, name),
            "{}/{}.json".format(output_dir, name))

"name": info[0],
                "type": info[1],
                "size": len(mapper),
                "i2s": mapper
            })


    t_train = project_table(trainset, meta)
    t_test = project_table(testset, meta)

    name = "census"
    with open("{}/{}.json".format(output_dir, name), 'w') as f:
        json.dump(meta, f, sort_keys=True, indent=4, separators=(',', ': '))
    np.savez("{}/{}.npz".format(output_dir, name), train=t_train, test=t_test)

    verify("{}/{}.npz".format(output_dir, name),
            "{}/{}.json".format(output_dir, name))

# assert 0

    output_dir = "data/simulated"
    if not os.path.exists(output_dir):
        try:
            os.mkdir(output_dir)
        except:
            pass
    # Store simulated data
    with open("{}/{}.json".format(output_dir, dist), 'w') as f:
        json.dump(maker.meta, f, sort_keys=True, indent=4, separators=(',', ': '))
    with open("{}/{}_structure.json".format(output_dir, dist), 'w') as f:
        f.write(maker.model.to_json())
    np.savez("{}/{}.npz".format(output_dir, dist), train=samples[:len(samples)//2], test=samples[len(samples)//2:])

    utils.verify("{}/{}.npz".format(output_dir, dist),
        "{}/{}.json".format(output_dir, dist))

tdata = project_table(df, meta)

    np.random.seed(0)
    np.random.shuffle(tdata)

    t_train = tdata[:-10000]
    t_test = tdata[-10000:]

    name = "adult"
    with open("{}/{}.json".format(output_dir, name), 'w') as f:
        json.dump(meta, f, sort_keys=True, indent=4, separators=(',', ': '))
    np.savez("{}/{}.npz".format(output_dir, name), train=t_train, test=t_test)

    verify("{}/{}.npz".format(output_dir, name),
            "{}/{}.json".format(output_dir, name))