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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 is_convertible_with(self, other):
 """Returns True iff `self` is convertible with `other`. Two possibly-partially-defined shapes are convertible if there exists a fully-defined shape that both shapes can represent. Thus, convertibility allows the shape inference code to reason about partially-defined shapes. For example: * TensorShape(None) is convertible with all shapes. * TensorShape([None, None]) is convertible with all two-dimensional shapes, such as TensorShape([32, 784]), and also TensorShape(None). It is not convertible with, for example, TensorShape([None]) or TensorShape([None, None, None]). * TensorShape([32, None]) is convertible with all two-dimensional shapes with size 32 in the 0th dimension, and also TensorShape([None, None]) and TensorShape(None). It is not convertible with, for example, TensorShape([32]), TensorShape([32, None, 1]) or TensorShape([64, None]). * TensorShape([32, 784]) is convertible with itself, and also TensorShape([32, None]), TensorShape([None, 784]), TensorShape([None, None]) and TensorShape(None). It is not convertible with, for example, TensorShape([32, 1, 784]) or TensorShape([None]). The convertibility relation is reflexive and symmetric, but not transitive. For example, TensorShape([32, 784]) is convertible with TensorShape(None), and TensorShape(None) is convertible with TensorShape([4, 4]), but TensorShape([32, 784]) is not convertible with TensorShape([4, 4]). Args: other: Another TensorShape. Returns: True iff `self` is convertible with `other`. """

        other = as_shape(other)
        if self._dims is not None and other.dims is not None:
            if self.ndims != other.ndims:
                return False
            for x_dim, y_dim in zip(self._dims, other.dims):
                if not x_dim.is_convertible_with(y_dim):
                    return False
        return True