class_docstr: Sampling from the Poincaré Disk using either a Wrapped Riemannian or Riemannian Gaussian
  distribution.
  
  Args:
      model (VAMP): The vae model to sample from.
      sampler_config (BaseSamplerConfig): An instance of BaseSamplerConfig in which any sampler's
          parameters is made available. If None a default configuration is used. Default: None
code: def __init__(
      self,
      model: PoincareVAE,
      sampler_config: PoincareDiskSamplerConfig = None,
  ):
      assert isinstance(
          model, PoincareVAE
      ), "This sampler is only suitable for PoincareVAE model"
  
      if sampler_config is None:
          sampler_config = PoincareDiskSamplerConfig()
  
      BaseSampler.__init__(
          self, model=model, sampler_config=sampler_config
      )
  
      self.gen_distribution = self.model.prior(
          loc=self.model._pz_mu,
          scale=self.model._pz_logvar.exp(),
          manifold=self.model.latent_manifold,
      )
  
file_path: benchmark_VAE/src/pythae/samplers/pvae_sampler/pvae_sampler.py
imports: import torch
  from ...models import PoincareVAE
  from ..base import BaseSampler
  from .pvae_sampler_config import PoincareDiskSamplerConfig
label_desc: Use image node_id 1 to create a new PoincareDiskSampler object from inherited base classes: BaseSampler with example: obj = PoincareDiskSampler(model, sampler_config)