def load_cifar10(path=".",
                 normalize=True,
                 contrast_normalize=False,
                 whiten=False):
    cfiar10_dataset = CIFAR10(path=path,
                              normalize=normalize,
                              contrast_normalize=contrast_normalize,
                              whiten=whiten)
    return cfiar10_dataset.load_data()
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def global_contrast_normalize(X, scale=1., min_divisor=1e-8):
    return CIFAR10.global_contrast_normalize(X, scale=scale, min_divisor=min_divisor)
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def zca_whiten(train, test, cache=None):
    return CIFAR10.zca_whiten(train, test, cache=cache)
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def _compute_zca_transform(imgs, filter_bias=0.1):
    return CIFAR10._compute_zca_transform(imgs, filter_bias=filter_bias)
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def global_contrast_normalize(X, scale=1., min_divisor=1e-8):
    return CIFAR10.global_contrast_normalize(X, scale=scale, min_divisor=min_divisor)
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def zca_whiten(train, test, cache=None):
    return CIFAR10.zca_whiten(train, test, cache=cache)
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def _compute_zca_transform(imgs, filter_bias=0.1):
    return CIFAR10._compute_zca_transform(imgs, filter_bias=filter_bias)