*Last updated: 2023-03-16.*
# tf_quant_finance.math.qmc.random_scrambling_matrices
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Returns a `Tensor` drawn from a uniform distribution.
```python
tf_quant_finance.math.qmc.random_scrambling_matrices(
dim, num_digits, seed, validate_args=False, dtype=None, name=None
)
```
The result can be can be passed to the `scramble_generating_matrices` function
to randomize a given `generating_matrices`.
#### Examples
```python
import tf_quant_finance as tff
# Example: Creating random matrices which can scramble 2D generating matrices.
dim = 2
num_digits = 10
seed = (2, 3)
tff.math.qmc.random_scrambling_matrices(dim, num_digits, seed=seed)
# ==> tf.Tensor([
# [586, 1011, 896, 818, 550, 1009, 880, 855, 686, 758],
# [872, 958, 870, 1000, 963, 919, 994, 583, 1007, 739],
# ], shape=(2, 10), dtype=int32)
```
#### Args:
* `dim`: Positive scalar `Tensor` of integers with rank 0. The event size of
points which can be sampled from the generating matrices to scramble.
* `num_digits`: Positive scalar `Tensor` of integers with rank 0. the base-2
precision of the points which can be sampled from the generating matrices
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
`tf.int32` or `tf.int64`).
Default value: `None` which maps to `tf.int32`.
* `name`: Python `str` name prefixed to ops created by this function.
Default value: `None` which maps to `random_scrambling_matrices`.
#### Returns:
A `Tensor` with `shape` `(dim, num_digits)`.