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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 most_specific_convertible_shape(self, other):
 """Returns the most specific TensorShape convertible with `self` and `other`. * TensorShape([None, 1]) is the most specific TensorShape convertible with both TensorShape([2, 1]) and TensorShape([5, 1]). Note that TensorShape(None) is also convertible with above mentioned TensorShapes. * TensorShape([1, 2, 3]) is the most specific TensorShape convertible with both TensorShape([1, 2, 3]) and TensorShape([1, 2, 3]). There are more less specific TensorShapes convertible with above mentioned TensorShapes, e.g. TensorShape([1, 2, None]), TensorShape(None). Args: other: Another `TensorShape`. Returns: A `TensorShape` which is the most specific convertible shape of `self` and `other`. """


        other = as_shape(other)
        if self._dims is None or other.dims is None or self.ndims != other.ndims:
            return unknown_shape()

        dims = [(Dimension(None))] * self.ndims
        for i, (d1, d2) in enumerate(zip(self._dims, other.dims)):
            if d1 is not None and d2 is not None and d1 == d2:
                dims[i] = d1
        return TensorShape(dims)