def norm(x, ord=None, axis=None, keepdims=False):
    """Matrix or vector norm.

    This function is able to return one of eight different matrix norms, or one
    of an infinite number of vector norms (described below), depending on the
    value of the `ord` parameter.

    Args:
        x: Input tensor.
        ord: Order of the norm (see table under Notes). The default is `None`.
        axis: If `axis` is an integer, it specifies the axis of `x` along which
            to compute the vector norms. If `axis` is a 2-tuple, it specifies
            the axes that hold 2-D matrices, and the matrix norms of these
            matrices are computed.
        keepdims: If this is set to `True`, the axes which are reduced are left
            in the result as dimensions with size one.

    Note:
        For values of `ord < 1`, the result is, strictly speaking, not a
        mathematical 'norm', but it may still be useful for various numerical
        purposes. The following norms can be calculated:
        - For matrices:
            - `ord=None`: Frobenius norm
            - `ord="fro"`: Frobenius norm
            - `ord="nuc"`: nuclear norm
            - `ord=np.inf`: `max(sum(abs(x), axis=1))`
            - `ord=-np.inf`: `min(sum(abs(x), axis=1))`
            - `ord=0`: not supported
            - `ord=1`: `max(sum(abs(x), axis=0))`
            - `ord=-1`: `min(sum(abs(x), axis=0))`
            - `ord=2`: 2-norm (largest sing. value)
            - `ord=-2`: smallest singular value
            - other: not supported
        - For vectors:
            - `ord=None`: 2-norm
            - `ord="fro"`: not supported
            - `ord="nuc"`: not supported
            - `ord=np.inf`: `max(abs(x))`
            - `ord=-np.inf`: `min(abs(x))`
            - `ord=0`: `sum(x != 0)`
            - `ord=1`: as below
            - `ord=-1`: as below
            - `ord=2`: as below
            - `ord=-2`: as below
            - other: `sum(abs(x)**ord)**(1./ord)`

    Returns:
        Norm of the matrix or vector(s).

    Example:

    >>> x = keras.ops.reshape(keras.ops.arange(9, dtype="float32") - 4, (3, 3))
    >>> keras.ops.linalg.norm(x)
    7.7459664
    """
    if any_symbolic_tensors((x,)):
        return Norm(ord=ord, axis=axis, keepdims=keepdims).symbolic_call(x)
    x = backend.convert_to_tensor(x)
    return backend.linalg.norm(x, ord=ord, axis=axis, keepdims=keepdims)
Matrix or vector norm.

    This function is able to return one of eight different matrix norms, or one
    of an infinite number of vector norms (described below), depending on the
    value of the `ord` parameter.

    Args:
        x: Input tensor.
        ord: Order of the norm (see table under Notes). The default is `None`.
        axis: If `axis` is an integer, it specifies the axis of `x` along which
            to compute the vector norms. If `axis` is a 2-tuple, it specifies
            the axes that hold 2-D matrices, and the matrix norms of these
            matrices are computed.
        keepdims: If this is set to `True`, the axes which are reduced are left
            in the result as dimensions with size one.

    Note:
        For values of `ord < 1`, the result is, strictly speaking, not a
        mathematical 'norm', but it may still be useful for various numerical
        purposes. The following norms can be calculated:
        - For matrices:
            - `ord=None`: Frobenius norm
            - `ord="fro"`: Frobenius norm
            - `ord="nuc"`: nuclear norm
            - `ord=np.inf`: `max(sum(abs(x), axis=1))`
            - `ord=-np.inf`: `min(sum(abs(x), axis=1))`
            - `ord=0`: not supported
            - `ord=1`: `max(sum(abs(x), axis=0))`
            - `ord=-1`: `min(sum(abs(x), axis=0))`
            - `ord=2`: 2-norm (largest sing. value)
            - `ord=-2`: smallest singular value
            - other: not supported
        - For vectors:
            - `ord=None`: 2-norm
            - `ord="fro"`: not supported
            - `ord="nuc"`: not supported
            - `ord=np.inf`: `max(abs(x))`
            - `ord=-np.inf`: `min(abs(x))`
            - `ord=0`: `sum(x != 0)`
            - `ord=1`: as below
            - `ord=-1`: as below
            - `ord=2`: as below
            - `ord=-2`: as below
            - other: `sum(abs(x)**ord)**(1./ord)`

    Returns:
        Norm of the matrix or vector(s).

    Example:

    >>> x = keras.ops.reshape(keras.ops.arange(9, dtype="float32") - 4, (3, 3))
    >>> keras.ops.linalg.norm(x)
    7.7459664
    
keras/src/ops/linalg.py