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

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BCEWithLogitsLoss(weight=None, reduction="mean", pos_weight=None)

    Binary cross entropy over logits: the sigmoid fused with the loss in one
    stable pass.

    The module form of
    ``clika_runtime.nn.functional.binary_cross_entropy_with_logits``: construct
    it once with the settings, then call it like a function.

    Args:
        weight: a per-element rescaling weight, broadcast against ``input``;
            unset weighs 1; None takes the function's own default.
        reduction: how the elementwise losses reduce.
        pos_weight: a weight on the positive class, broadcast against
            ``input``; unset weighs 1; None takes the function's own default.
    

## `__init__`

```python
__init__(self, weight: 'Tensor | None' = None, reduction: '_Reduction' = 'mean', pos_weight: 'Tensor | 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', target: 'Tensor') -> 'Tensor'
```

forward(input, target) -> Tensor

Runs ``clika_runtime.nn.functional.binary_cross_entropy_with_logits``
on ``input``, ``target`` with the stored settings; returns a new
tensor.