---
title: "Embedding"
sidebar_label: "Embedding"
description: "The clika_runtime.nn Embedding class."
---

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Embedding(num_embeddings, embedding_dim, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False, _weight=None, _freeze=False, device=None, dtype=None)

    Gathers rows of its ``[num_embeddings, embedding_dim]`` table by index.

    Args:
        num_embeddings: the table's row count (the vocabulary size).
        embedding_dim: the size of each row.
        padding_idx: the index of the padding row; kept as an attribute (a
            lookup is a plain gather here, so the row holds whatever the
            checkpoint carries).
        max_norm: not served (it renormalizes rows at every lookup); pass
            ``None``.
        norm_type: the norm ``max_norm`` would use; unused without it.
        scale_grad_by_freq: a gradient option; only ``False`` is served.
        sparse: a gradient layout option; only ``False`` is served.
        _weight: a ``[num_embeddings, embedding_dim]`` table to bind at
            construction.
        _freeze: accepted for signature compatibility; this runtime trains
            nothing, so every table is frozen.
        device: where the table lives; ``None`` declares on the cpu.
        dtype: the table dtype the layer declares; ``None`` declares the
            default dtype (:func:`~clika_runtime.get_default_dtype`). A
            ``load_state_dict`` casts a dense table to the declared dtype;
            ``assign=True`` adopts the table's own dtype instead, and a
            quantized table binds as it is (a set dtype then pins the
            lookup's output dtype).
    

## `__init__`

```python
__init__(self, num_embeddings: 'int', embedding_dim: 'int', padding_idx: 'int | None' = None, max_norm: 'float | None' = None, norm_type: 'float' = 2.0, scale_grad_by_freq: 'bool' = False, sparse: 'bool' = False, _weight: 'Tensor | None' = None, _freeze: 'bool' = False, device: 'Device | str | None' = None, dtype: 'DtypeLike | None' = None) -> 'None'
```

Initialize self.  See help(type(self)) for accurate signature.

## `extra_repr`

```python
extra_repr(self) -> 'str'
```

extra_repr() -> str

One line of per-class detail for :meth:`__repr__`; a layer prints
its geometry here (``in_features=64, out_features=256``).

## `forward`

```python
forward(self, input: 'Tensor') -> 'Tensor'
```

forward(input) -> Tensor

Gather rows: ``[*]`` int32 / int64 indices to ``[*, embedding_dim]``.

## `from_pretrained`

```python
from_pretrainedfrom_pretrained(embeddings, freeze=True, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False) -> Embedding
```

from_pretrained(embeddings, freeze=True, padding_idx=None, max_norm=None, norm_type=2.0, scale_grad_by_freq=False, sparse=False) -> Embedding

Construct FROM a ``[num_embeddings, embedding_dim]`` table, bound in
the same call; ``freeze`` is accepted for signature compatibility
(this runtime trains nothing).

## `from_weights`

```python
from_weightsfrom_weights(weight, *, dtype=None, device=None) -> Embedding
```

from_weights(weight, *, dtype=None, device=None) -> Embedding

Construct FROM a table (dense or quantized): geometry, dtype, and
device read off ``weight``, bound in the same call. A set ``dtype``
casts a dense table (a quantized table pins the lookup's output
dtype instead).

## `set_weights`

```python
set_weights(self, weight: 'Tensor') -> 'None'
```

set_weights(weight) -> None

Bind the table directly (dense, or a quantized payload carrying
its scheme); raises RuntimeError on a shape mismatch.