class_docstr: Sampling from the VAMP prior.
  
  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: VAMP, sampler_config: VAMPSamplerConfig = None
  ):
      assert isinstance(
          model, VAMP
      ), "This sampler is only suitable for VAMP model"
  
      if sampler_config is None:
          sampler_config = VAMPSamplerConfig()
  
      BaseSampler.__init__(
          self, model=model, sampler_config=sampler_config
      )
  
file_path: benchmark_VAE/src/pythae/samplers/vamp_sampler/vamp_sampler.py
imports: import torch
  from ...models import VAMP
  from ..base import BaseSampler
  from .vamp_sampler_config import VAMPSamplerConfig
label_desc: Use image node_id 1 to create a new VAMPSampler object from inherited base classes: BaseSampler with example: obj = VAMPSampler(model, sampler_config)