code: def norm_v2(
      tensor, ord="euclidean", axis=None, keepdims=None, name=None
  ):
      r"""Computes the norm of vectors, matrices, and tensors.
  
      This function can compute several different vector norms (the 1-norm, the
      Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0) and
      matrix norms (Frobenius, 1-norm, 2-norm and inf-norm).
  
      Args:
        tensor: `Tensor` of types `float32`, `float64`, `complex64`, `complex128`
        ord: Order of the norm. Supported values are `'fro'`, `'euclidean'`,
          `1`, `2`, `np.inf` and any positive real number yielding the corresponding
          p-norm. Default is `'euclidean'` which is equivalent to Frobenius norm if
          `tensor` is a matrix and equivalent to 2-norm for vectors.
          Some restrictions apply:
            a) The Frobenius norm `'fro'` is not defined for vectors,
            b) If axis is a 2-tuple (matrix norm), only `'euclidean'`, '`fro'`, `1`,
               `2`, `np.inf` are supported.
          See the description of `axis` on how to compute norms for a batch of
          vectors or matrices stored in a tensor.
        axis: If `axis` is `None` (the default), the input is considered a vector
          and a single vector norm is computed over the entire set of values in the
          tensor, i.e. `norm(tensor, ord=ord)` is equivalent to
          `norm(reshape(tensor, [-1]), ord=ord)`.
          If `axis` is a Python integer, the input is considered a batch of vectors,
          and `axis` determines the axis in `tensor` over which to compute vector
          norms.
          If `axis` is a 2-tuple of Python integers it is considered a batch of
          matrices and `axis` determines the axes in `tensor` over which to compute
          a matrix norm.
          Negative indices are supported. Example: If you are passing a tensor that
          can be either a matrix or a batch of matrices at runtime, pass
          `axis=[-2,-1]` instead of `axis=None` to make sure that matrix norms are
          computed.
        keepdims: If True, the axis indicated in `axis` are kept with size 1.
          Otherwise, the dimensions in `axis` are removed from the output shape.
        name: The name of the op.
  
      Returns:
        output: A `Tensor` of the same type as tensor, containing the vector or
          matrix norms. If `keepdims` is True then the rank of output is equal to
          the rank of `tensor`. Otherwise, if `axis` is none the output is a scalar,
          if `axis` is an integer, the rank of `output` is one less than the rank
          of `tensor`, if `axis` is a 2-tuple the rank of `output` is two less
          than the rank of `tensor`.
  
      Raises:
        ValueError: If `ord` or `axis` is invalid.
  
      @compatibility(numpy)
      Mostly equivalent to numpy.linalg.norm.
      Not supported: ord <= 0, 2-norm for matrices, nuclear norm.
      Other differences:
        a) If axis is `None`, treats the flattened `tensor` as a vector
         regardless of rank.
        b) Explicitly supports 'euclidean' norm as the default, including for
         higher order tensors.
      @end_compatibility
      """
      return norm(
          tensor=tensor,
          ord=ord,
          axis=axis,
          keepdims=keepdims,
          name=name,
      )
  
imports: import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import gen_array_ops
  from tensorflow.python.ops import gen_linalg_ops
  from tensorflow.python.ops import linalg_ops_impl
  from tensorflow.python.ops import map_fn
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops.gen_linalg_ops import *
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 11 for calling a global function with example usage: norm_v2(tensor, ord, axis, keepdims, name) and returns: norm