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concrete/docs/dev/api/concrete.fhe.mlir.utils.md
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# <kbd>module</kbd> `concrete.fhe.mlir.utils`
Declaration of various functions and constants related to MLIR conversion.
**Global Variables**
---------------
- **MAXIMUM_TLU_BIT_WIDTH**
---
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## <kbd>function</kbd> `flood_replace_none_values`
```python
flood_replace_none_values(table: list)
```
Use flooding algorithm to replace `None` values.
**Args:**
table (list): the list in which there are `None` values that need to be replaced with copies of the closest non `None` data from the list
---
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## <kbd>function</kbd> `construct_table_multivariate`
```python
construct_table_multivariate(node: Node, preds: List[Node]) List[Any]
```
Construct the lookup table for a multivariate node.
**Args:**
node (Node): Multivariate node to construct the table for
preds (List[Node]): ordered predecessors to `node`
**Returns:**
List[Any]: lookup table corresponding to `node` and its input value
---
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## <kbd>function</kbd> `construct_table`
```python
construct_table(
node: Node,
preds: List[Node],
configuration: Configuration
) List[Any]
```
Construct the lookup table for an Operation.Generic node.
**Args:**
node (Node): Operation.Generic to construct the table
preds (List[Node]): ordered predecessors to `node`
configuration (Configuration): configuration to use
**Returns:**
List[Any]: lookup table corresponding to `node` and its input value
---
<a href="../../../compilers/concrete-compiler/compiler/lib/Bindings/Python/concrete/fhe/mlir/utils.py#L270"><img align="right" style="float:right;" src="https://img.shields.io/badge/-source-cccccc?style=flat-square"></a>
## <kbd>function</kbd> `construct_deduplicated_tables`
```python
construct_deduplicated_tables(
node: Node,
preds: List[Node],
configuration: Configuration
) Tuple[Tuple[ndarray, Optional[List[Tuple[int, ]]]], ]
```
Construct lookup tables for each cell of the input for an Operation.Generic node.
**Args:**
node (Node): Operation.Generic to construct the table
preds (List[Node]): ordered predecessors to `node`
configuration (Configuration): configuration to use
**Returns:**
Tuple[Tuple[numpy.ndarray, List[Tuple[int, ...]]], ...]: tuple containing tuples of 2 for
- constructed table
- list of indices of the input that use the constructed table
e.g.,
- <b>`.. code-block`</b>: : python
( (np.array([3, 1, 2, 4]), [(1, 0), (2, 1)]), (np.array([5, 8, 6, 7]), [(0, 0), (0, 1), (1, 1), (2, 0)]), )
means the lookup on 3x2 input will result in
- <b>`.. code-block`</b>: : python
[ [5, 8, 6, 7][input[0, 0]] , [5, 8, 6, 7][input[0, 1]] ] [ [3, 1, 2, 4][input[1, 0]] , [5, 8, 6, 7][input[1, 1]] ] [ [5, 8, 6, 7][input[2, 0]] , [3, 1, 2, 4][input[2, 1]] ]
---
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## <kbd>class</kbd> `HashableNdarray`
HashableNdarray class, to use numpy arrays in dictionaries.
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### <kbd>method</kbd> `__init__`
```python
__init__(array: ndarray)
```
---
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## <kbd>class</kbd> `Comparison`
Comparison enum, to store the result comparison in 2-bits as there are three possible outcomes.