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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 wrap(self, value):
 """Wrap numpy.ndarray as Value """

        attrib = getattr(value, 'attrib', {})

        S, NS = divmod(time.time(), 1.0)
        value = numpy.asarray(value) # loses any special/augmented attributes
        dims = list(value.shape)
        dims.reverse() # inner-most sent as left

        if 'ColorMode' not in attrib:
            # attempt to infer color mode from shape
            if value.ndim==2:
                attrib['ColorMode'] = 0 # gray

            elif value.ndim==3:
                for idx,dim in enumerate(dims):
                    if dim==3: # assume it's a color
                        attrib['ColorMode'] = 2 + idx  # 2 - RGB1, 3 - RGB2, 4 - RGB3
                        break # assume that the first is color, and any subsequent dim=3 is a thin ROI

        dataSize = value.nbytes

        return Value(self.type, {
            'value': (self._code2u[value.dtype.char], value.flatten()),
            'compressedSize': dataSize,
            'uncompressedSize': dataSize,
            'uniqueId': 0,
            'timeStamp': {
                'secondsPastEpoch': S,
                'nanoseconds': NS * 1e9,
            },
            'attribute': [{'name': K, 'value': V} for K, V in attrib.items()],
            'dimension': [{'size': N,
                           'offset': 0,
                           'fullSize': N,
                           'binning': 1,
                           'reverse': False} for N in dims],
        })