class_docstr: Sampling from uniform distribution on hypersphere.
  
  Args:
      model (BaseAE): 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: BaseAE,
      sampler_config: HypersphereUniformSamplerConfig = None,
  ):
      if sampler_config is None:
          sampler_config = HypersphereUniformSamplerConfig()
  
      BaseSampler.__init__(
          self, model=model, sampler_config=sampler_config
      )
  
file_path: benchmark_VAE/src/pythae/samplers/hypersphere_uniform_sampler/hypersphere_uniform_sampler.py
imports: import torch
  from ...models import BaseAE
  from ..base import BaseSampler
  from .hypersphere_uniform_config import HypersphereUniformSamplerConfig
label_desc: Use image node_id 1 to create a new HypersphereUniformSampler object from inherited base classes: BaseSampler with example: obj = HypersphereUniformSampler(model, sampler_config)