code: def norm(x, ord=None, axis=None, keepdims=False):
      r"""
      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.
  
      Parameters
      ----------
      x : array_like
          Input tensor.  If `axis` is None, `x` must be 1-D or 2-D.
      ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional
          Order of the norm (see table under ``Notes``). inf means mars tensor's
          `inf` object.
      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 then either a vector norm (when `x`
          is 1-D) or a matrix norm (when `x` is 2-D) 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 Tensor
          Norm of the matrix or vector(s).
  
      Notes
      -----
      For values of ``ord <= 0``, 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:
  
      =====  ============================  ==========================
      ord    norm for matrices             norm for vectors
      =====  ============================  ==========================
      None   Frobenius norm                2-norm
      'fro'  Frobenius norm                --
      'nuc'  nuclear norm                  --
      inf    max(sum(abs(x), axis=1))      max(abs(x))
      -inf   min(sum(abs(x), axis=1))      min(abs(x))
      0      --                            sum(x != 0)
      1      max(sum(abs(x), axis=0))      as below
      -1     min(sum(abs(x), axis=0))      as below
      2      2-norm (largest sing. value)  as below
      -2     smallest singular value       as below
      other  --                            sum(abs(x)**ord)**(1./ord)
      =====  ============================  ==========================
  
      The Frobenius norm is given by [1]_:
  
          :math:`||A||_F = [\\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
  
      The nuclear norm is the sum of the singular values.
  
      References
      ----------
      .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
             Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
  
      Examples
      --------
      >>> from mars.tensor import linalg as LA
      >>> import mars.tensor as mt
      >>> a = mt.arange(9) - 4
      >>> a.execute()
      array([-4, -3, -2, -1,  0,  1,  2,  3,  4])
      >>> b = a.reshape((3, 3))
      >>> b.execute()
      array([[-4, -3, -2],
             [-1,  0,  1],
             [ 2,  3,  4]])
  
      >>> LA.norm(a).execute()
      7.745966692414834
      >>> LA.norm(b).execute()
      7.745966692414834
      >>> LA.norm(b, 'fro').execute()
      7.745966692414834
      >>> LA.norm(a, mt.inf).execute()
      4.0
      >>> LA.norm(b, mt.inf).execute()
      9.0
      >>> LA.norm(a, -mt.inf).execute()
      0.0
      >>> LA.norm(b, -mt.inf).execute()
      2.0
  
      >>> LA.norm(a, 1).execute()
      20.0
      >>> LA.norm(b, 1).execute()
      7.0
      >>> LA.norm(a, -1).execute()
      0.0
      >>> LA.norm(b, -1).execute()
      6.0
      >>> LA.norm(a, 2).execute()
      7.745966692414834
      >>> LA.norm(b, 2).execute()
      7.3484692283495345
  
      >>> LA.norm(a, -2).execute()
      0.0
      >>> LA.norm(b, -2).execute()
      4.351066026358965e-18
      >>> LA.norm(a, 3).execute()
      5.8480354764257312
      >>> LA.norm(a, -3).execute()
      0.0
  
      Using the `axis` argument to compute vector norms:
  
      >>> c = mt.array([[ 1, 2, 3],
      ...               [-1, 1, 4]])
      >>> LA.norm(c, axis=0).execute()
      array([ 1.41421356,  2.23606798,  5.        ])
      >>> LA.norm(c, axis=1).execute()
      array([ 3.74165739,  4.24264069])
      >>> LA.norm(c, ord=1, axis=1).execute()
      array([ 6.,  6.])
  
      Using the `axis` argument to compute matrix norms:
  
      >>> m = mt.arange(8).reshape(2,2,2)
      >>> LA.norm(m, axis=(1,2)).execute()
      array([  3.74165739,  11.22497216])
      >>> LA.norm(m[0, :, :]).execute(), LA.norm(m[1, :, :]).execute()
      (3.7416573867739413, 11.224972160321824)
  
      """
      x = astensor(x)
      ndim = x.ndim
  
      if ord == "fro":
          ord = None
      if axis is not None:
          if isinstance(axis, Iterable):
              axis = tuple(validate_axis(ndim, a) for a in axis)
          else:
              axis = (validate_axis(ndim, axis),)
      else:
          axis = tuple(range(x.ndim))
  
      op = TensorNorm(
          ord=ord,
          axis=axis,
          keepdims=keepdims,
          dtype=np.result_type(x.dtype, np.float_),
          sparse=x.issparse(),
      )
      return op(x)
  
imports: import itertools
  from collections.abc import Iterable
  import numpy
  from ...None import opcodes
  from ...core import recursive_tile
  from ...serialization.serializables import FieldTypes, KeyField, AnyField, TupleField, BoolField
  from ..utils import validate_axis
  from ..array_utils import device, as_same_device
  from ..operands import TensorHasInput, TensorOperandMixin
  from ..arithmetic import sqrt
  from ..datasource import tensor
  from .svd import svd
label_desc: Use image node_id 1 for calling a global function with example usage: norm(x, ord, axis, keepdims) and returns: op