code: def _norm(r, ord, axis, keepdims):
      if ord is None:
          return sqrt((abs(r) ** 2).sum(axis=axis, keepdims=keepdims))
      elif ord == "nuc":
          if len(axis) == 1:
              raise ValueError("Invalid norm order for vectors.")
          return svd(r)[1][np.newaxis].sum(keepdims=keepdims)
      elif ord == np.inf:
          if r.ndim > 2:
              raise ValueError("Improper number of dimensions to norm.")
          r = abs(r)
          if len(axis) == 1:
              return r.max(axis=axis, keepdims=keepdims)
          else:
              return r.sum(axis=axis[1], keepdims=keepdims).max(
                  keepdims=keepdims
              )
      elif ord == -np.inf:
          if r.ndim > 2:
              raise ValueError("Improper number of dimensions to norm.")
          r = abs(r)
          if len(axis) == 1:
              return r.min(axis=axis, keepdims=keepdims)
          else:
              return r.sum(axis=axis[1], keepdims=keepdims).min(
                  keepdims=keepdims
              )
      elif ord == 0:
          if r.ndim > 2:
              raise ValueError("Improper number of dimensions to norm.")
          if len(axis) == 2:
              raise ValueError("Invalid norm order for matrices.")
          return (
              (r != 0).astype(r.dtype).sum(axis=axis, keepdims=keepdims)
          )
      elif ord == 1:
          if r.ndim > 2:
              raise ValueError("Improper number of dimensions to norm.")
          r = abs(r)
          if len(axis) == 1:
              return r.sum(axis=axis, keepdims=keepdims)
          else:
              return r.sum(axis=axis[0], keepdims=keepdims).max(
                  keepdims=keepdims
              )
      elif ord == -1 and len(axis) == 2:
          if r.ndim > 2:
              raise ValueError("Improper number of dimensions to norm.")
          return (
              abs(r)
              .sum(axis=axis[0], keepdims=keepdims)
              .min(keepdims=keepdims)
          )
      elif ord == 2 and len(axis) == 2:
          return svd(r)[1][np.newaxis].max(keepdims=keepdims)
      elif ord == -2 and len(axis) == 2:
          return svd(r)[1][np.newaxis].min(keepdims=keepdims)
      else:
          if len(axis) == 2:
              raise ValueError("Invalid norm order for matrices.")
  
          return (abs(r) ** ord).sum(axis=axis, keepdims=keepdims) ** (
              1.0 / ord
          )
  
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 8 for calling the TensorNorm obj's underlying member method code with example usage: obj._norm(r, ord, axis, keepdims) and returns: sqrt, svd, r, r, r, r, unknown, r, r, abs, svd, svd, unknown