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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 concat_aligned(items):
 """Concatenate arrays, ensuring the output is 64-byte aligned. We only align float arrays; other arrays are concatenated as normal. This should be used instead of np.concatenate() to improve performance when the output array is likely to be fed into TensorFlow. """


    if len(items) == 0:
        return []
    elif len(items) == 1:
        # we assume the input is aligned. In any case, it doesn't help
        # performance to force align it since that incurs a needless copy.
        return items[0]
    elif (isinstance(items[0], np.ndarray)
          and items[0].dtype in [np.float32, np.float64, np.uint8]):
        dtype = items[0].dtype
        flat = aligned_array(sum(s.size for s in items), dtype)
        batch_dim = sum(s.shape[0] for s in items)
        new_shape = (batch_dim, ) + items[0].shape[1:]
        output = flat.reshape(new_shape)
        assert output.ctypes.data % 64 == 0, output.ctypes.data
        np.concatenate(items, out=output)
        return output
    else:
        return np.concatenate(items)