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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_with(self, other):
 """Returns a Dimension that combines the information in `self` and `other`. Dimensions are combined as follows: ```python tf.Dimension(n) .merge_with(tf.Dimension(n)) == tf.Dimension(n) tf.Dimension(n) .merge_with(tf.Dimension(None)) == tf.Dimension(n) tf.Dimension(None).merge_with(tf.Dimension(n)) == tf.Dimension(n) tf.Dimension(None).merge_with(tf.Dimension(None)) == tf.Dimension(None) tf.Dimension(n) .merge_with(tf.Dimension(m)) # raises ValueError for n != m ``` Args: other: Another Dimension. Returns: A Dimension containing the combined information of `self` and `other`. Raises: ValueError: If `self` and `other` are not convertible (see is_convertible_with). """

        other = as_dimension(other)
        self.assert_is_convertible_with(other)
        if self._value is None:
            return Dimension(other.value)
        else:
            return Dimension(self._value)