code: def random_scrambling_vectors(
      dim: types.IntTensor,
      seed: int,
      validate_args: bool = False,
      dtype: tf.DType = None,
      name: str = None,
  ) -> types.RealTensor:
      r"""Returns a `Tensor` drawn from a uniform distribution.
  
      The returned `Tensor` can be can be added to the specified
      `generating_vectors` in order to randomize it.
  
      #### Examples
  
      ```python
      import tf_quant_finance as tff
  
      # Example: Creating random vectors which can scramble 2D generating vectors.
  
      dim = 2
      seed = (2, 3)
  
      tff.math.qmc.random_scrambling_vectors(dim, seed=seed)
      # ==> tf.Tensor([0.17481351, 0.9780868], shape=(2,), dtype=float32)
      ```
  
      Args:
        dim: Positive scalar `Tensor` of integers with rank 0. The event size of
          points which can be sampled from the generating vectors to scramble.
        seed: Positive scalar `Tensor` with shape [2] and dtype `int32` used as seed
          for the random generator.
        validate_args: Python `bool` indicating whether to validate arguments.
          Default value: `False`.
        dtype: Optional `dtype`. The `dtype` of the output `Tensor` (either
          `float32` or `float64`).
          Default value: `None` which maps to `tf.float32`.
        name: Python `str` name prefixed to ops created by this function.
          Default value: `None` which maps to `random_scrambling_vectors`.
  
      Returns:
        A `Tensor` of real values between 0 (incl.) and 1 (excl.) with `shape`
        `(dim,)`.
      """
  
      with tf.name_scope(name or "random_scrambling_vectors"):
          control_deps = []
          if validate_args:
              control_deps.append(
                  tf.debugging.assert_positive(
                      dim, message="dim must be positive"
                  )
              )
  
          with tf.control_dependencies(control_deps):
              return tf.random.stateless_uniform(
                  (dim,),
                  seed,
                  minval=0.0,
                  maxval=1.0,
                  dtype=dtype or tf.float32,
              )
  
imports: import tensorflow.compat.v2
  from tf_quant_finance import types
  from tf_quant_finance.math.qmc import utils
label_desc: Use image node_id 1 for calling a global function with example usage: random_scrambling_vectors(dim, seed, validate_args, dtype, name) and returns: tf