Add transformation using cdf of distribution. (#72495)
Summary:
This PR adds a transform that uses the cumulative distribution function of a given probability distribution.
For example, the following code constructs a simple Gaussian copula.
```python
# Construct a Gaussian copula from a multivariate normal.
base_dist = MultivariateNormal(
loc=torch.zeros(2),
scale_tril=LKJCholesky(2).sample(),
)
transform = CumulativeDistributionTransform(Normal(0, 1))
copula = TransformedDistribution(base_dist, [transform])
```
The following snippet creates a "wrapped" Gaussian copula for correlated positive variables with Weibull marginals.
```python
transforms = [
CumulativeDistributionTransform(Normal(0, 1)),
CumulativeDistributionTransform(Weibull(4, 2)).inv,
]
wrapped_copula = TransformedDistribution(base_dist, transforms)
```
cc fritzo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72495
Reviewed By: ejguan
Differential Revision: D34085919
Pulled By: albanD
fbshipit-source-id: 7917391519a96b0d9b54c52db65d1932f961d070
(cherry picked from commit 572196146ede48a279828071941f6eeb8fc98a56)
