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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 _merge_multi_context(outputs, major_axis):
 """Merge outputs that lives on multiple context into one, so that they look like living on one context. """

    rets = []
    for tensors, axis in zip(outputs, major_axis):
        if axis >= 0:
            # pylint: disable=no-member,protected-access
            if len(tensors) == 1:
                rets.append(tensors[0])
            else:
                # Concatenate if necessary
                rets.append(nd.concat(*[tensor.as_in_context(tensors[0].context)
                                        for tensor in tensors],
                                      dim=axis))
            # pylint: enable=no-member,protected-access
        else:
            # negative axis means the there is no batch_size axis, and all the
            # results should be the same on each device. We simply take the
            # first one, without checking they are actually the same
            rets.append(tensors[0])
    return rets