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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 dot(self, other):
 """ Dot product with a SparseVector or 1- or 2-dimensional Numpy array. 25.0 22.0 0.0 array([ 22., 22.]) Traceback (most recent call last):
 AssertionError: dimension mismatch Traceback (most recent call last):
 AssertionError: dimension mismatch Traceback (most recent call last):
 AssertionError: dimension mismatch Traceback (most recent call last):
 AssertionError: dimension mismatch """


        if isinstance(other, np.ndarray):
            if other.ndim not in [2, 1]:
                raise ValueError("Cannot call dot with %d-dimensional array" % other.ndim)
            assert len(self) == other.shape[0], "dimension mismatch"
            return np.dot(self.values, other[self.indices])

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

        if isinstance(other, DenseVector):
            return np.dot(other.array[self.indices], self.values)

        elif isinstance(other, SparseVector):
            # Find out common indices.
            self_cmind = np.in1d(self.indices, other.indices, assume_unique=True)
            self_values = self.values[self_cmind]
            if self_values.size == 0:
                return 0.0
            else:
                other_cmind = np.in1d(other.indices, self.indices, assume_unique=True)
                return np.dot(self_values, other.values[other_cmind])

        else:
            return self.dot(_convert_to_vector(other))