code: def vector_norm(
      x: torch.Tensor,
      /,
      *,
      axis: Optional[Union[int, Sequence[int]]] = None,
      keepdims: bool = False,
      ord: Union[int, float, Literal[inf, -inf]] = 2,
      dtype: Optional[torch.dtype] = None,
      out: Optional[torch.Tensor] = None,
  ) -> torch.Tensor:
      # TODO: remove the as_native_dtype call once there are wrappers that handle dtype
      #  conversion automatically in the backends
      dtype = ivy.as_native_dtype(dtype)
      if dtype and x.dtype != dtype:
          x = x.type(dtype)
      return torch.linalg.vector_norm(x, ord, axis, keepdims, out=out)
  
imports: import torch
  from typing import Union, Optional, Tuple, Literal, List, NamedTuple, Sequence
  from collections import namedtuple
  import ivy
  from ivy import inf
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
  from .elementwise import _cast_for_unary_op
label_desc: Use image node_id 26 for calling a global function with example usage: vector_norm() and returns: torch