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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 create(name='local'):
 """Creates a new KVStore. For single machine training, there are two commonly used types: ``local``: Copies all gradients to CPU memory and updates weights there. ``device``: Aggregates gradients and updates weights on GPUs. With this setting, the KVStore also attempts to use GPU peer-to-peer communication, potentially accelerating the communication. For distributed training, KVStore also supports a number of types: ``dist_sync``: Behaves similarly to ``local`` but with one major difference. With ``dist_sync``, batch-size now means the batch size used on each machine. So if there are ``n`` machines and we use batch size ``b``, then ``dist_sync`` behaves like ``local`` with batch size ``n * b``. ``dist_device_sync``: Identical to ``dist_sync`` with the difference similar to ``device`` vs ``local``. ``dist_async``: Performs asynchronous updates. The weights are updated whenever gradients are received from any machine. No two updates happen on the same weight at the same time. However, the order is not guaranteed. Parameters name : {'local', 'device', 'nccl', 'dist_sync', 'dist_device_sync', 'dist_async'} The type of KVStore. Returns ------- kv : KVStore The created KVStore. """

    if not isinstance(name, string_types):
        raise TypeError('name must be a string')
    handle = KVStoreHandle()
    check_call(_LIB.MXKVStoreCreate(c_str(name),
                                    ctypes.byref(handle)))
    kv = KVStore(handle)
    set_kvstore_handle(kv.handle)
    return kv