# CatTransform

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

```python
class tensorplay.distributions.CatTransform(tseq: Sequence[Transform], dim: int = 0, lengths: Sequence[int] | None = None, cache_size: int = 0)
```

Transform functor that applies a sequence of transforms tseq
component-wise to each submatrix at dim, of length lengths[dim],
in a way compatible with tensorplay.cat().

Example:

```
x0 = tensorplay.cat([tensorplay.range(1, 10), tensorplay.range(1, 10)], dim=0)
x = tensorplay.cat([x0, x0], dim=0)
t0 = CatTransform([ExpTransform(), identity_transform], dim=0, lengths=[10, 10])
t = CatTransform([t0, t0], dim=0, lengths=[20, 20])
y = t(x)
```

```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.

```python
property sign: int
```

Returns the sign of the determinant of the Jacobian, if applicable.
In general this only makes sense for bijective transforms.