code: def norm(tensor, like=None, **kwargs):
      """Compute the norm of a tensor in each interface."""
      if like == "jax":
          from jax.numpy.linalg import norm
  
      elif like == "tensorflow":
          from tensorflow import norm
  
      elif like == "torch":
          from torch.linalg import norm
  
          if "axis" in kwargs:
              axis_val = kwargs.pop("axis")
              kwargs["dim"] = axis_val
  
      elif (
          like == "autograd"
          and kwargs.get("ord", None) is None
          and kwargs.get("axis", None) is None
      ):
          norm = _flat_autograd_norm
  
      else:
          from scipy.linalg import norm
  
      return norm(tensor, **kwargs)
  
imports: import functools
  from collections.abc import Sequence
  import autoray
  import numpy
  from autograd.numpy.numpy_boxes import ArrayBox
  from autoray import numpy
  from numpy import ndarray
  from .None import single_dispatch
  from .utils import cast, cast_like, get_interface, requires_grad
label_desc: Use image node_id 23 for calling a global function with example usage: norm(tensor, like) and returns: norm