code: def vector_to_skew_symmetric_matrix(
      vector: Union[tf.Tensor, tf.Variable],
      /,
      *,
      out: Optional[Union[tf.Tensor, tf.Variable]] = None,
  ) -> Union[tf.Tensor, tf.Variable]:
      batch_shape = list(vector.shape[:-1])
      # BS x 3 x 1
      vector_expanded = tf.expand_dims(vector, -1)
      # BS x 1 x 1
      a1s = vector_expanded[..., 0:1, :]
      a2s = vector_expanded[..., 1:2, :]
      a3s = vector_expanded[..., 2:3, :]
      # BS x 1 x 1
      zs = tf.zeros(batch_shape + [1, 1], dtype=vector.dtype)
      # BS x 1 x 3
      row1 = tf.concat((zs, -a3s, a2s), -1)
      row2 = tf.concat((a3s, zs, -a1s), -1)
      row3 = tf.concat((-a2s, a1s, zs), -1)
      # BS x 3 x 3
      ret = tf.concat((row1, row2, row3), -2)
      return ret
  
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 28 for calling a global function with example usage: vector_to_skew_symmetric_matrix() and returns: ret