*Last updated: 2023-03-16.*
# tf_quant_finance.math.random.sobol.sample
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Returns num_results samples from the Sobol sequence of dimension dim.
```python
tf_quant_finance.math.random.sobol.sample(
dim, num_results, skip=0, validate_args=False, dtype=None, name=None
)
```
Uses the original ordering of points, not the more commonly used Gray code
ordering. Derived from notes by Joe & Kuo[1].
[1] describes bitwise operations on binary floats. The implementation below
achieves this by transforming the floats into ints, being careful to align
the digits so the bitwise operations are correct, then transforming back to
floats.
#### Args:
* `dim`: Positive Python `int` representing each sample's `event_size.`
* `num_results`: Positive scalar `Tensor` of dtype int32. The number of Sobol
points to return in the output.
* `skip`: Positive scalar `Tensor` of dtype int32. The number of initial points
of the Sobol sequence to skip.
* `validate_args`: Python `bool`. When `True`, input `Tensor's` are checked for
validity despite possibly degrading runtime performance. The checks verify
that `dim >= 1`, `num_results >= 1`, `skip >= 0` and whether
`num_results + skip < 2**31 - 1`. When `False` invalid inputs may silently
render incorrect outputs.
Default value: False.
* `dtype`: Optional `dtype`. The dtype of the output `Tensor` (either `float32`
or `float64`).
Default value: `None` which maps to the `float32`.
* `name`: Python `str` name prefixed to ops created by this function.
#### Returns:
`Tensor` of samples from Sobol sequence with `shape` [n, dim].
#### References
[1]: S. Joe and F. Y. Kuo. Notes on generating Sobol sequences. August 2008.
https://web.maths.unsw.edu.au/~fkuo/sobol/joe-kuo-notes.pdf