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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 squared_distance(self, other):
 """ Squared distance from a SparseVector or 1-dimensional NumPy array. 0.0 11.0 11.0 26.0 26.0 Traceback (most recent call last):
 AssertionError: dimension mismatch Traceback (most recent call last):
 AssertionError: dimension mismatch """

        assert len(self) == _vector_size(other), "dimension mismatch"

        if isinstance(other, np.ndarray) or isinstance(other, DenseVector):
            if isinstance(other, np.ndarray) and other.ndim != 1:
                raise Exception("Cannot call squared_distance with %d-dimensional array" %
                                other.ndim)
            if isinstance(other, DenseVector):
                other = other.array
            sparse_ind = np.zeros(other.size, dtype=bool)
            sparse_ind[self.indices] = True
            dist = other[sparse_ind] - self.values
            result = np.dot(dist, dist)

            other_ind = other[~sparse_ind]
            result += np.dot(other_ind, other_ind)
            return result

        elif isinstance(other, SparseVector):
            result = 0.0
            i, j = 0, 0
            while i < len(self.indices) and j < len(other.indices):
                if self.indices[i] == other.indices[j]:
                    diff = self.values[i] - other.values[j]
                    result += diff * diff
                    i += 1
                    j += 1
                elif self.indices[i] < other.indices[j]:
                    result += self.values[i] * self.values[i]
                    i += 1
                else:
                    result += other.values[j] * other.values[j]
                    j += 1
            while i < len(self.indices):
                result += self.values[i] * self.values[i]
                i += 1
            while j < len(other.indices):
                result += other.values[j] * other.values[j]
                j += 1
            return result
        else:
            return self.squared_distance(_convert_to_vector(other))