*Last updated: 2023-03-16.*
# tf_quant_finance.math.qmc.random_scrambling_vectors
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Returns a `Tensor` drawn from a uniform distribution.
```python
tf_quant_finance.math.qmc.random_scrambling_vectors(
dim, seed, validate_args=False, dtype=None, name=None
)
```
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,)`.