*Last updated: 2023-03-16.*
# tf_quant_finance.math.qmc.random_digital_shift
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Returns a `Tensor` drawn from a uniform distribution.
```python
tf_quant_finance.math.qmc.random_digital_shift(
dim, num_digits, seed, validate_args=False, dtype=None, name=None
)
```
The result can be can be passed to the `sample_digital_net` function to shift
sampled points through a bitwise xor.
#### Examples
```python
import tf_quant_finance as tff
# Example: Creating a Digital shift which can randomize sampled 2D points.
dim = 2
num_digits = 10
seed = (2, 3)
tff.math.qmc.random_digital_shift(dim, num_digits, seed=seed)
# ==> tf.Tensor([586, 1011], shape=(2,), dtype=int32)
```
#### Args:
* `dim`: Positive scalar `Tensor` of integers with rank 0. The event size of the
sampled points to shift.
* `num_digits`: Positive scalar `Tensor` of integers with rank 0. the base-2
precision of the sampled points to shift.
* `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_digital_shift`.
#### Returns:
A `Tensor` with `shape` `(dim,)`.