# uniform

`random.uniform(shape, lower_bound, upper_bound, seed=None, *, name=None)`

Create a sample of uniformly distributed random numbers.

### Parameters

**shape**(*tuple*) – The shape of the sampled random numbers.**lower_bound**(*float*) – The inclusive lower bound of the interval of the uniform distribution.**upper_bound**(*float*) – The exclusive upper bound of the interval of the uniform distribution.**seed**(*int**or**None**,**optional*) – A seed for the random number generator. Defaults to None, in which case a random value for the seed is used.**name**(*str**or**None**,**optional*) – The name of the node.

### Returns

A tensor containing a sample of uniformly distributed random numbers
with shape `shape`

.

### Return type

### SEE ALSO

`Graph.random.choices`

: Create random samples from the data that you provide.

`Graph.random.normal`

: Create a sample of normally distributed random numbers.

`boulderopal.run_stochastic_optimization`

: Function to find the minimum of generic stochastic functions.

## Examples

Create a random tensor by sampling uniformly from $[0,, 1)$

```
>>> samples = graph.random.uniform(
... shape=(3, 1), lower_bound=0, upper_bound=1, seed=0, name="samples"
... )
>>> result = bo.execute_graph(graph=graph, output_node_names="samples")
>>> result["output"]["samples"]["value"]
array([[0.8069013], [0.79011373], [0.38818516]])
```

See more examples in the How to optimize controls robust to strong noise sources user guide.