human
Complete this python program:
def diag(*values, **kwargs):
assistant
#Here is the Python code you requested.

    """Create a sparse, diagonal matrix from a list of diagonal values.
    Notes
    When arguments are matrices they are fitted in resultant matrix.
    The returned matrix is a mutable, dense matrix. To make it a different
    type, send the desired class for keyword ``cls``.
    Examples
    >>> from sympy.matrices import diag, Matrix, ones
    >>> diag(1, 2, 3)
    Matrix([
    [1, 0, 0],
    [0, 2, 0],
    [0, 0, 3]])
    >>> diag(*[1, 2, 3])
    Matrix([
    [1, 0, 0],
    [0, 2, 0],
    [0, 0, 3]])
    The diagonal elements can be matrices; diagonal filling will
    continue on the diagonal from the last element of the matrix:
    >>> from sympy.abc import x, y, z
    >>> a = Matrix([x, y, z])
    >>> b = Matrix([[1, 2], [3, 4]])
    >>> c = Matrix([[5, 6]])
    >>> diag(a, 7, b, c)
    Matrix([
    [x, 0, 0, 0, 0, 0],
    [y, 0, 0, 0, 0, 0],
    [z, 0, 0, 0, 0, 0],
    [0, 7, 0, 0, 0, 0],
    [0, 0, 1, 2, 0, 0],
    [0, 0, 3, 4, 0, 0],
    [0, 0, 0, 0, 5, 6]])
    When diagonal elements are lists, they will be treated as arguments
    to Matrix:
    >>> diag([1, 2, 3], 4)
    Matrix([
    [1, 0],
    [2, 0],
    [3, 0],
    [0, 4]])
    >>> diag([[1, 2, 3]], 4)
    Matrix([
    [1, 2, 3, 0],
    [0, 0, 0, 4]])
    A given band off the diagonal can be made by padding with a
    vertical or horizontal "kerning" vector:
    >>> hpad = ones(0, 2)
    >>> vpad = ones(2, 0)
    >>> diag(vpad, 1, 2, 3, hpad) + diag(hpad, 4, 5, 6, vpad)
    Matrix([
    [0, 0, 4, 0, 0],
    [0, 0, 0, 5, 0],
    [1, 0, 0, 0, 6],
    [0, 2, 0, 0, 0],
    [0, 0, 3, 0, 0]])
    The type is mutable by default but can be made immutable by setting
    the ``mutable`` flag to False:
    >>> type(diag(1))
    <class \sympy.matrices.dense.MutableDenseMatrix\>
    >>> from sympy.matrices import ImmutableMatrix
    >>> type(diag(1, cls=ImmutableMatrix))
    <class \sympy.matrices.immutable.ImmutableDenseMatrix\>
    See Also
    eye
    """
    from .dense import Matrix
    def normalize(m):
        if (is_sequence(m) and (not isinstance(m, MatrixBase))):
            return Matrix(m)
        return m
    values = (normalize(m) for m in values)
    return Matrix.diag(*values, **kwargs)