loss/consistency_loss.py [35:43]:
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    assert (
        input.ndimension() == target.ndimension()
        and input.ndimension() == weights.ndimension()
    )
    # normalize to sum=1
    B = weights.shape[0]
    weights_sum = torch.sum(weights.view(B, -1), dim=-1).view(B, 1, 1, 1)
    weights_sum = torch.clamp(weights_sum, min=eps)
    weights_n = weights / weights_sum
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loss/consistency_loss.py [59:67]:
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    assert (
        input.ndimension() == target.ndimension()
        and input.ndimension() == weights.ndimension()
    )
    # normalize to sum=1
    B = weights.shape[0]
    weights_sum = torch.sum(weights.view(B, -1), dim=-1).view(B, 1, 1, 1)
    weights_sum = torch.clamp(weights_sum, min=eps)
    weights_n = weights / weights_sum
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