docstr: A list of samplers for sampling from a `PosteriorList`.
  
  Args:
      samplers: A variable number of samplers. This should include
          a sampler for each posterior.
docstr_tok: ["A","list","of","samplers","for","sampling","from","a","`","PosteriorList","`",".","Args",":","samplers",":","A","variable","number","of","samplers",".","This","should","include","a","sampler","for","each","posterior","."]
code: def __init__(self, *samplers: MCSampler) -> None:
      r"""A list of samplers for sampling from a `PosteriorList`.
  
      Args:
          samplers: A variable number of samplers. This should include
              a sampler for each posterior.
      """
      super(MCSampler, self).__init__()
      self.samplers = ModuleList(samplers)
      self._validate_samplers()
  
file_path: botorch/botorch/sampling/list_sampler.py
imports: from __future__ import annotations
  import torch
  from botorch.exceptions.errors import UnsupportedError
  from botorch.posteriors.posterior_list import PosteriorList
  from botorch.sampling.base import MCSampler
  from torch import Tensor
  from torch.nn import ModuleList
label_desc: Use image node_id 1 to create a new ListSampler object from inherited base classes: MCSampler with example: obj = ListSampler()