# module `concrete.fhe.mlir.processors.assign_bit_widths`
Declaration of `AssignBitWidths` graph processor.
---
## class `AssignBitWidths`
AssignBitWidths graph processor, to assign proper bit-widths to be compatible with FHE.
There are two modes:
- Single Precision, where all encrypted values have the same precision.
- Multi Precision, where encrypted values can have different precisions.
There is preference list for comparison strategies.
- Strategies will be traversed in order and bit-widths will be assigned according to the first available strategy.
### method `__init__`
```python
__init__(
single_precision: bool,
composable: bool,
comparison_strategy_preference: List[ComparisonStrategy],
bitwise_strategy_preference: List[BitwiseStrategy],
shifts_with_promotion: bool,
multivariate_strategy_preference: List[MultivariateStrategy],
min_max_strategy_preference: List[MinMaxStrategy]
)
```
---
### method `apply`
```python
apply(graph: Graph)
```
---
## class `AdditionalConstraints`
AdditionalConstraints class to customize bit-width assignment step easily.
### method `__init__`
```python
__init__(
optimizer: Optimize,
graph: Graph,
bit_widths: Dict[Node, Int],
comparison_strategy_preference: List[ComparisonStrategy],
bitwise_strategy_preference: List[BitwiseStrategy],
shifts_with_promotion: bool,
multivariate_strategy_preference: List[MultivariateStrategy],
min_max_strategy_preference: List[MinMaxStrategy]
)
```
---
### method `all_inputs_are_encrypted`
```python
all_inputs_are_encrypted(node: Node, preds: List[Node]) → bool
```
---
### method `bitwise`
```python
bitwise(node: Node, preds: List[Node])
```
---
### method `comparison`
```python
comparison(node: Node, preds: List[Node])
```
---
### method `constraint`
```python
constraint(node: Node, constraint: BoolRef)
```
---
### method `generate_for`
```python
generate_for(node: Node, bit_width: )
```
Generate additional constraints for a node.
**Args:**
node (Node): node to generate constraints for
bit_width (z3.Int): symbolic bit-width which will be assigned to node once constraints are solved
---
### method `has_overflow_protection`
```python
has_overflow_protection(node: Node, preds: List[Node]) → bool
```
---
### method `inputs_and_output_share_precision`
```python
inputs_and_output_share_precision(node: Node, preds: List[Node])
```
---
### method `inputs_require_one_more_bit`
```python
inputs_require_one_more_bit(node: Node, preds: List[Node])
```
---
### method `inputs_share_precision`
```python
inputs_share_precision(node: Node, preds: List[Node])
```
---
### method `min_max`
```python
min_max(node: Node, preds: List[Node])
```
---
### method `multivariate`
```python
multivariate(node: Node, preds: List[Node])
```
---
### method `some_inputs_are_clear`
```python
some_inputs_are_clear(node: Node, preds: List[Node]) → bool
```