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Here is an alternative solution: import numpy as np from torch.utils.data.sampler import WeightedRandomSampler counts = np.bincount (y) labels_weights = 1. / counts weights = labels_weights [y] WeightedRandomSampler (weights, len (weights)) where y is a list of labels corresponding to each sample, has shape (n_samples,) and are encoded [0.

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