def evaluate_checkpoint(params, batch):
    if isinstance(params, dict):
        weight, bias = float(params["weight"].item()), float(params["bias"].item())
    else:
        weight, bias = map(float, params)
    return [[sum((weight * left / 100_000 + bias - right / 100_000) ** 2
                 for left, right in zip(sample[:-1], sample[1:])) / (len(sample) - 1)]
            for sample in batch.tolist()]

def aggregate_losses(outputs):
    losses = [row[0] for batch in outputs for row in batch]
    return sum(losses) / len(losses)