# TanhTransform

Source: https://www.tensorplay.cn/docs/generated/tensorplay.distributions.TanhTransform.html

```python
class tensorplay.distributions.TanhTransform(cache_size: int = 0)
```

Transform via the mapping $y = \tanh(x)$.

It is equivalent to

```
ComposeTransform(
    [
        AffineTransform(0.0, 2.0),
        SigmoidTransform(),
        AffineTransform(-1.0, 2.0),
    ]
)
```

However this might not be numerically stable, thus it is recommended to use TanhTransform
instead.

Note that one should use cache_size=1 when it comes to NaN/Inf values.

```python
forward_shape(shape)
```

Infers the shape of the forward computation, given the input shape.
Defaults to preserving shape.

```python
property inv: Transform
```

Returns the inverse [Transform](/docs/generated/tensorplay.distributions.Transform.html#tensorplay.distributions.Transform) of this transform.
This should satisfy t.inv.inv is t.

```python
inverse_shape(shape)
```

Infers the shapes of the inverse computation, given the output shape.
Defaults to preserving shape.