code: def vector_norm(
      x: Union[tf.Tensor, tf.Variable],
      /,
      *,
      axis: Optional[Union[int, Sequence[int]]] = None,
      keepdims: bool = False,
      ord: Union[int, float, Literal[inf, -inf]] = 2,
      dtype: Optional[DType] = None,
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      if dtype and x.dtype != dtype:
          x = tf.cast(x, dtype)
      abs_x = tf.abs(x)
      if ord == 0:
          return tf.reduce_sum(
              tf.cast(x != 0, abs_x.dtype), axis=axis, keepdims=keepdims
          )
      elif ord == inf:
          return tf.reduce_max(abs_x, axis=axis, keepdims=keepdims)
      elif ord == -inf:
          return tf.reduce_min(abs_x, axis=axis, keepdims=keepdims)
      else:
          return tf.reduce_sum(
              abs_x**ord, axis=axis, keepdims=keepdims
          ) ** (1.0 / ord)
  
imports: from typing import Union, Optional, Tuple, Literal, List, NamedTuple, Sequence
  from collections import namedtuple
  import tensorflow
  from tensorflow.python.framework.dtypes import DType
  import ivy
  from ivy import inf
  from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
  from .None import backend_version
label_desc: Use image node_id 25 for calling a global function with example usage: vector_norm() and returns: tf, tf, tf, unknown