*Last updated: 2023-03-16.*
# tf_quant_finance.math.qmc.lattice_rule_sample
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Constructs a lattice rule from a generating vector.
```python
tf_quant_finance.math.qmc.lattice_rule_sample(
generating_vectors, dim, num_results, sequence_indices=None,
additive_shift=None, apply_tent_transform=False, validate_args=False,
dtype=None, name=None
)
```
#### Examples
```python
import tensorflow as tf
import tf_quant_finance as tff
# Example: Sampling 1,000 points from 2D generating vectors.
generating_vectors = tf.constant([1, 387275, 314993, 50301], dtype=tf.int32)
dim = 2
num_results = 1000
tff.math.qmc.lattice_rule_sample(generating_vectors, dim, num_results)
# ==> tf.Tensor([
# [0., 0. ],
# [0.001, 0.2749939 ],
# [0.002, 0.5499878 ],
# ...
# [0.99700004, 0.1689148 ],
# [0.998, 0.4439087 ],
# [0.9990001, 0.7189026 ],
# ], shape=(1000, 2), dtype=float32)
```
#### Args:
* `generating_vectors`: Positive scalar `Tensor` of integers with rank 1
representing the vector from which to sample points.
* `dim`: Positive scalar `Tensor` of integers with rank 0. The event size of the
sampled points. Must not exceed the size of `generating_vectors`.
* `num_results`: Positive scalar `Tensor` of integers with rank 0. The maximum
number of points to sample.
* `sequence_indices`: Optional positive scalar `Tensor` of integers with rank 1.
The elements of the sequence to return specified by their position in the
sequence.
Default value: `None` which corresponds to the `[0, num_results)` range.
* `additive_shift`: Optional scalar `Tensor` of real values with the same
`shape` as `generating_vectors`. The additive shift to add to all the
points (modulo 1) before applying the tent transform.
Default value: `None`.
* `apply_tent_transform`: Python `bool` indicating whether to apply a tent
transform to the sampled points.
Default value: `False`.
* `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 `float32`.
* `name`: Python `str` name prefixed to ops created by this function.
Default value: `None` which maps to `sample_lattice_rule`.
#### Returns:
A `Tensor` of samples from the Sobol sequence with `shape`
`(num_samples,)` where `num_samples = min(num_results,
size(sequence_indices))`.