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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_accuracy(targets, outputs, k=1, ignore_index=None):
 """ Get the accuracy top-k accuracy between two tensors. Args: targets (1 - 2D :class:`torch.Tensor`):
 Target or true vector against which to measure saccuracy outputs (1 - 3D :class:`torch.Tensor`):
 Prediction or output vector ignore_index (int, optional):
 Specifies a target index that is ignored Returns: :class:`tuple` consisting of accuracy (:class:`float`), number correct (:class:`int`) and total (:class:`int`) Example: 0.8 4 5 """

    n_correct = 0.0
    for target, output in zip(targets, outputs):
        if not torch.is_tensor(target) or is_scalar(target):
            target = torch.LongTensor([target])

        if not torch.is_tensor(output) or is_scalar(output):
            output = torch.LongTensor([[output]])

        predictions = output.topk(k=min(k, len(output)), dim=0)[0]
        for prediction in predictions:
            if torch_equals_ignore_index(
                    target.squeeze(), prediction.squeeze(), ignore_index=ignore_index):
                n_correct += 1
                break

    return n_correct / len(targets), int(n_correct), len(targets)