code: def vector_norm(
      x: np.ndarray,
      /,
      *,
      axis: Optional[Union[int, Sequence[int]]] = None,
      keepdims: bool = False,
      ord: Union[int, float, Literal[inf, -inf]] = 2,
      dtype: Optional[np.dtype] = None,
      out: Optional[np.ndarray] = None,
  ) -> np.ndarray:
      if dtype and x.dtype != dtype:
          x = x.astype(dtype)
      abs_x = np.abs(x)
      if isinstance(axis, list):
          axis = tuple(axis)
      if ord == 0:
          return np.sum(
              (abs_x != 0).astype(abs_x.dtype),
              axis=axis,
              keepdims=keepdims,
              out=out,
          )
      elif ord == inf:
          return np.max(abs_x, axis=axis, keepdims=keepdims, out=out)
      elif ord == -inf:
          return np.min(abs_x, axis=axis, keepdims=keepdims, out=out)
      else:
          # There is a rounding error whenever the input is a 0-dim
          # The solution at the moment is to convert the 0-dim array to 1-dim
          # and then convert it back to 0-dim in case of keepdims=True
          if x.ndim == 0:
              abs_x = np.expand_dims(abs_x, axis=0)
  
          res = (
              np.sum(abs_x**ord, axis=axis, keepdims=keepdims)
              ** (1.0 / ord)
          ).astype(abs_x.dtype)
  
          if keepdims and x.ndim == 0:
              res = np.squeeze(res)
  
          return res
  
imports: from collections import namedtuple
  from typing import Union, Optional, Tuple, Literal, List, NamedTuple, Sequence
  import numpy
  import ivy
  from ivy import inf
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.backends.numpy.helpers import _scalar_output_to_0d_array
  from .None import backend_version
label_desc: Use image node_id 26 for calling a global function with example usage: vector_norm() and returns: np, np, np, res