code: def norm(x, ord=None, axis=None, keepdims=False):
      r"""
      Matrix or vector norm.
  
      This function can only support Frobenius norm for now.
      The Frobenius norm is given by [1]_:
  
          :math:`||A||_F = [\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
  
      Parameters
      ----------
      x : ndarray
          Input array.
      ord : {'fro'}, optional
          Order of the norm.
      axis : {int, 2-tuple of ints, None}, optional
          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.  If `axis` is None, the norm of the whole ndarray is
          returned.
  
      keepdims : bool, optional
          If this is set to True, the axes which are normed over are left in the
          result as dimensions with size one.  With this option the result will
          broadcast correctly against the original `x`.
  
      Returns
      -------
      n : float or ndarray
          Norm of the matrix or vector(s).
  
      Notes
      -----
      This operator differs from NumPy in the aspect that it always returns a
      zero-dim tensor for the cases where Python float values are expected
      in NumPy.
  
      References
      ----------
      .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
             Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
  
      Examples
      --------
      >>> from numpy import linalg as LA
      >>> a = np.arange(9) - 4
      >>> a
      array([-4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.])
      >>> b = a.reshape((3, 3))
      >>> b
      array([[-4., -3., -2.],
             [-1.,  0.,  1.],
             [ 2.,  3.,  4.]])
      >>> LA.norm(a)
      array(7.745967)
      >>>
      >>> LA.norm(b)
      array(7.745967)
      >>> LA.norm(b, 'fro')
      array(7.745967)
      """
      return _mx_nd_np.linalg.norm(x, ord, axis, keepdims)
  
imports: from functools import reduce
  from ..ndarray import numpy
  from ..util import wrap_data_api_linalg_func
  from .fallback_linalg import *
  from .None import fallback_linalg
label_desc: Use image node_id 11 for calling a global function with example usage: norm(x, ord, axis, keepdims) and returns: _mx_nd_np